
# Import the libraries
import pandas as pd # Used for data manipulation and analysis
import numpy as np # Handles numerical operations like mathematical computations
import matplotlib.pyplot as plt # Creates static, animated, and intereactive visualizations
import seaborn as sns # Makes statistical graphics
import plotly.express as px # Generate interactive plots
import plotly.graph_objects as go # Generate interactive plots
import statistics as st # Provides functions to perform statistical operations
import geopandas as gpd # Deals with geospatial data
import ast # Allows Python code to be parsed into an abstract syntax tree (AST)
from collections import Counter # Keeps track of the frequency of elements in a collection
import warnings # Used to warn the user about situations that aren't necessarily exceptions


# Set up the pandas
pd.set_option('display.max_columns', None) # Display all columns in DataFrame.
pd.set_option('display.max_rows', None) # Display all rows in DataFrame.

# Set up the matplotlib
plt.style.use('ggplot') # Apply 'ggplot' style to plots.

# Set up warnings
warnings.filterwarnings('ignore') # Ignore all warning messages.


# Import DataFrame
data = pd.read_csv("/kaggle/input/melbourne-airbnb-september-2023-dataset/listings.csv")


# Check the DataFrame dimension
data.shape

(23185, 75)


# Check data types
data.dtypes

id                                                int64
listing_url                                      object
scrape_id                                         int64
last_scraped                                     object
source                                           object
name                                             object
description                                      object
neighborhood_overview                            object
picture_url                                      object
host_id                                           int64
host_url                                         object
host_name                                        object
host_since                                       object
host_location                                    object
host_about                                       object
host_response_time                               object
host_response_rate                               object
host_acceptance_rate                             object
host_is_superhost                                object
host_thumbnail_url                               object
host_picture_url                                 object
host_neighbourhood                               object
host_listings_count                             float64
host_total_listings_count                       float64
host_verifications                               object
host_has_profile_pic                             object
host_identity_verified                           object
neighbourhood                                    object
neighbourhood_cleansed                           object
neighbourhood_group_cleansed                    float64
latitude                                        float64
longitude                                       float64
property_type                                    object
room_type                                        object
accommodates                                      int64
bathrooms                                       float64
bathrooms_text                                   object
bedrooms                                        float64
beds                                            float64
amenities                                        object
price                                            object
minimum_nights                                    int64
maximum_nights                                    int64
minimum_minimum_nights                            int64
maximum_minimum_nights                            int64
minimum_maximum_nights                            int64
maximum_maximum_nights                            int64
minimum_nights_avg_ntm                          float64
maximum_nights_avg_ntm                          float64
calendar_updated                                float64
has_availability                                 object
availability_30                                   int64
availability_60                                   int64
availability_90                                   int64
availability_365                                  int64
calendar_last_scraped                            object
number_of_reviews                                 int64
number_of_reviews_ltm                             int64
number_of_reviews_l30d                            int64
first_review                                     object
last_review                                      object
review_scores_rating                            float64
review_scores_accuracy                          float64
review_scores_cleanliness                       float64
review_scores_checkin                           float64
review_scores_communication                     float64
review_scores_location                          float64
review_scores_value                             float64
license                                         float64
instant_bookable                                 object
calculated_host_listings_count                    int64
calculated_host_listings_count_entire_homes       int64
calculated_host_listings_count_private_rooms      int64
calculated_host_listings_count_shared_rooms       int64
reviews_per_month                               float64
dtype: object


# Preview the first 3 rows
data.head(3)


# Preview the last 3 rows
data.tail(3)


# Check summary statistics
data.describe()


# Check DataFrame information
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 23185 entries, 0 to 23184
Data columns (total 75 columns):
 #   Column                                        Non-Null Count  Dtype  
---  ------                                        --------------  -----  
 0   id                                            23185 non-null  int64  
 1   listing_url                                   23185 non-null  object 
 2   scrape_id                                     23185 non-null  int64  
 3   last_scraped                                  23185 non-null  object 
 4   source                                        23185 non-null  object 
 5   name                                          23185 non-null  object 
 6   description                                   22822 non-null  object 
 7   neighborhood_overview                         13256 non-null  object 
 8   picture_url                                   23185 non-null  object 
 9   host_id                                       23185 non-null  int64  
 10  host_url                                      23185 non-null  object 
 11  host_name                                     23183 non-null  object 
 12  host_since                                    23183 non-null  object 
 13  host_location                                 17286 non-null  object 
 14  host_about                                    12833 non-null  object 
 15  host_response_time                            15418 non-null  object 
 16  host_response_rate                            15418 non-null  object 
 17  host_acceptance_rate                          16579 non-null  object 
 18  host_is_superhost                             22468 non-null  object 
 19  host_thumbnail_url                            23183 non-null  object 
 20  host_picture_url                              23183 non-null  object 
 21  host_neighbourhood                            8772 non-null   object 
 22  host_listings_count                           23183 non-null  float64
 23  host_total_listings_count                     23183 non-null  float64
 24  host_verifications                            23185 non-null  object 
 25  host_has_profile_pic                          23183 non-null  object 
 26  host_identity_verified                        23183 non-null  object 
 27  neighbourhood                                 13257 non-null  object 
 28  neighbourhood_cleansed                        23185 non-null  object 
 29  neighbourhood_group_cleansed                  0 non-null      float64
 30  latitude                                      23185 non-null  float64
 31  longitude                                     23185 non-null  float64
 32  property_type                                 23185 non-null  object 
 33  room_type                                     23185 non-null  object 
 34  accommodates                                  23185 non-null  int64  
 35  bathrooms                                     0 non-null      float64
 36  bathrooms_text                                23174 non-null  object 
 37  bedrooms                                      16888 non-null  float64
 38  beds                                          23002 non-null  float64
 39  amenities                                     23185 non-null  object 
 40  price                                         23185 non-null  object 
 41  minimum_nights                                23185 non-null  int64  
 42  maximum_nights                                23185 non-null  int64  
 43  minimum_minimum_nights                        23185 non-null  int64  
 44  maximum_minimum_nights                        23185 non-null  int64  
 45  minimum_maximum_nights                        23185 non-null  int64  
 46  maximum_maximum_nights                        23185 non-null  int64  
 47  minimum_nights_avg_ntm                        23185 non-null  float64
 48  maximum_nights_avg_ntm                        23185 non-null  float64
 49  calendar_updated                              0 non-null      float64
 50  has_availability                              23185 non-null  object 
 51  availability_30                               23185 non-null  int64  
 52  availability_60                               23185 non-null  int64  
 53  availability_90                               23185 non-null  int64  
 54  availability_365                              23185 non-null  int64  
 55  calendar_last_scraped                         23185 non-null  object 
 56  number_of_reviews                             23185 non-null  int64  
 57  number_of_reviews_ltm                         23185 non-null  int64  
 58  number_of_reviews_l30d                        23185 non-null  int64  
 59  first_review                                  18675 non-null  object 
 60  last_review                                   18675 non-null  object 
 61  review_scores_rating                          18675 non-null  float64
 62  review_scores_accuracy                        18429 non-null  float64
 63  review_scores_cleanliness                     18433 non-null  float64
 64  review_scores_checkin                         18427 non-null  float64
 65  review_scores_communication                   18432 non-null  float64
 66  review_scores_location                        18427 non-null  float64
 67  review_scores_value                           18427 non-null  float64
 68  license                                       0 non-null      float64
 69  instant_bookable                              23185 non-null  object 
 70  calculated_host_listings_count                23185 non-null  int64  
 71  calculated_host_listings_count_entire_homes   23185 non-null  int64  
 72  calculated_host_listings_count_private_rooms  23185 non-null  int64  
 73  calculated_host_listings_count_shared_rooms   23185 non-null  int64  
 74  reviews_per_month                             18675 non-null  float64
dtypes: float64(20), int64(21), object(34)
memory usage: 13.3+ MB


# Check DataFrame column names
data.columns

Index(['id', 'listing_url', 'scrape_id', 'last_scraped', 'source', 'name',
       'description', 'neighborhood_overview', 'picture_url', 'host_id',
       'host_url', 'host_name', 'host_since', 'host_location', 'host_about',
       'host_response_time', 'host_response_rate', 'host_acceptance_rate',
       'host_is_superhost', 'host_thumbnail_url', 'host_picture_url',
       'host_neighbourhood', 'host_listings_count',
       'host_total_listings_count', 'host_verifications',
       'host_has_profile_pic', 'host_identity_verified', 'neighbourhood',
       'neighbourhood_cleansed', 'neighbourhood_group_cleansed', 'latitude',
       'longitude', 'property_type', 'room_type', 'accommodates', 'bathrooms',
       'bathrooms_text', 'bedrooms', 'beds', 'amenities', 'price',
       'minimum_nights', 'maximum_nights', 'minimum_minimum_nights',
       'maximum_minimum_nights', 'minimum_maximum_nights',
       'maximum_maximum_nights', 'minimum_nights_avg_ntm',
       'maximum_nights_avg_ntm', 'calendar_updated', 'has_availability',
       'availability_30', 'availability_60', 'availability_90',
       'availability_365', 'calendar_last_scraped', 'number_of_reviews',
       'number_of_reviews_ltm', 'number_of_reviews_l30d', 'first_review',
       'last_review', 'review_scores_rating', 'review_scores_accuracy',
       'review_scores_cleanliness', 'review_scores_checkin',
       'review_scores_communication', 'review_scores_location',
       'review_scores_value', 'license', 'instant_bookable',
       'calculated_host_listings_count',
       'calculated_host_listings_count_entire_homes',
       'calculated_host_listings_count_private_rooms',
       'calculated_host_listings_count_shared_rooms', 'reviews_per_month'],
      dtype='object')


# Identify unique values by each column
data.nunique()

id                                              23185
listing_url                                     23185
scrape_id                                           1
last_scraped                                        2
source                                              2
name                                            16587
description                                     21358
neighborhood_overview                           10985
picture_url                                     22651
host_id                                         13395
host_url                                        13395
host_name                                        5353
host_since                                       3942
host_location                                     957
host_about                                       6468
host_response_time                                  4
host_response_rate                                 62
host_acceptance_rate                               94
host_is_superhost                                   2
host_thumbnail_url                              13074
host_picture_url                                13074
host_neighbourhood                                117
host_listings_count                                97
host_total_listings_count                         135
host_verifications                                  8
host_has_profile_pic                                2
host_identity_verified                              2
neighbourhood                                     515
neighbourhood_cleansed                             30
neighbourhood_group_cleansed                        0
latitude                                        16261
longitude                                       16767
property_type                                      83
room_type                                           4
accommodates                                       16
bathrooms                                           0
bathrooms_text                                     38
bedrooms                                           12
beds                                               23
amenities                                       21892
price                                             924
minimum_nights                                     83
maximum_nights                                    192
minimum_minimum_nights                             79
maximum_minimum_nights                             88
minimum_maximum_nights                            176
maximum_maximum_nights                            174
minimum_nights_avg_ntm                            300
maximum_nights_avg_ntm                            601
calendar_updated                                    0
has_availability                                    2
availability_30                                    31
availability_60                                    61
availability_90                                    91
availability_365                                  366
calendar_last_scraped                               2
number_of_reviews                                 442
number_of_reviews_ltm                             137
number_of_reviews_l30d                             20
first_review                                     3168
last_review                                      2172
review_scores_rating                              163
review_scores_accuracy                            157
review_scores_cleanliness                         187
review_scores_checkin                             168
review_scores_communication                       152
review_scores_location                            116
review_scores_value                               162
license                                             0
instant_bookable                                    2
calculated_host_listings_count                     63
calculated_host_listings_count_entire_homes        62
calculated_host_listings_count_private_rooms       19
calculated_host_listings_count_shared_rooms         9
reviews_per_month                                 822
dtype: int64


# Check for missing values
data.isnull().sum()

id                                                  0
listing_url                                         0
scrape_id                                           0
last_scraped                                        0
source                                              0
name                                                0
description                                       363
neighborhood_overview                            9929
picture_url                                         0
host_id                                             0
host_url                                            0
host_name                                           2
host_since                                          2
host_location                                    5899
host_about                                      10352
host_response_time                               7767
host_response_rate                               7767
host_acceptance_rate                             6606
host_is_superhost                                 717
host_thumbnail_url                                  2
host_picture_url                                    2
host_neighbourhood                              14413
host_listings_count                                 2
host_total_listings_count                           2
host_verifications                                  0
host_has_profile_pic                                2
host_identity_verified                              2
neighbourhood                                    9928
neighbourhood_cleansed                              0
neighbourhood_group_cleansed                    23185
latitude                                            0
longitude                                           0
property_type                                       0
room_type                                           0
accommodates                                        0
bathrooms                                       23185
bathrooms_text                                     11
bedrooms                                         6297
beds                                              183
amenities                                           0
price                                               0
minimum_nights                                      0
maximum_nights                                      0
minimum_minimum_nights                              0
maximum_minimum_nights                              0
minimum_maximum_nights                              0
maximum_maximum_nights                              0
minimum_nights_avg_ntm                              0
maximum_nights_avg_ntm                              0
calendar_updated                                23185
has_availability                                    0
availability_30                                     0
availability_60                                     0
availability_90                                     0
availability_365                                    0
calendar_last_scraped                               0
number_of_reviews                                   0
number_of_reviews_ltm                               0
number_of_reviews_l30d                              0
first_review                                     4510
last_review                                      4510
review_scores_rating                             4510
review_scores_accuracy                           4756
review_scores_cleanliness                        4752
review_scores_checkin                            4758
review_scores_communication                      4753
review_scores_location                           4758
review_scores_value                              4758
license                                         23185
instant_bookable                                    0
calculated_host_listings_count                      0
calculated_host_listings_count_entire_homes         0
calculated_host_listings_count_private_rooms        0
calculated_host_listings_count_shared_rooms         0
reviews_per_month                                4510
dtype: int64


# Check percentage of missing values
round(data.isnull().sum() / len(data) * 100,2)

id                                                0.00
listing_url                                       0.00
scrape_id                                         0.00
last_scraped                                      0.00
source                                            0.00
name                                              0.00
description                                       1.57
neighborhood_overview                            42.83
picture_url                                       0.00
host_id                                           0.00
host_url                                          0.00
host_name                                         0.01
host_since                                        0.01
host_location                                    25.44
host_about                                       44.65
host_response_time                               33.50
host_response_rate                               33.50
host_acceptance_rate                             28.49
host_is_superhost                                 3.09
host_thumbnail_url                                0.01
host_picture_url                                  0.01
host_neighbourhood                               62.17
host_listings_count                               0.01
host_total_listings_count                         0.01
host_verifications                                0.00
host_has_profile_pic                              0.01
host_identity_verified                            0.01
neighbourhood                                    42.82
neighbourhood_cleansed                            0.00
neighbourhood_group_cleansed                    100.00
latitude                                          0.00
longitude                                         0.00
property_type                                     0.00
room_type                                         0.00
accommodates                                      0.00
bathrooms                                       100.00
bathrooms_text                                    0.05
bedrooms                                         27.16
beds                                              0.79
amenities                                         0.00
price                                             0.00
minimum_nights                                    0.00
maximum_nights                                    0.00
minimum_minimum_nights                            0.00
maximum_minimum_nights                            0.00
minimum_maximum_nights                            0.00
maximum_maximum_nights                            0.00
minimum_nights_avg_ntm                            0.00
maximum_nights_avg_ntm                            0.00
calendar_updated                                100.00
has_availability                                  0.00
availability_30                                   0.00
availability_60                                   0.00
availability_90                                   0.00
availability_365                                  0.00
calendar_last_scraped                             0.00
number_of_reviews                                 0.00
number_of_reviews_ltm                             0.00
number_of_reviews_l30d                            0.00
first_review                                     19.45
last_review                                      19.45
review_scores_rating                             19.45
review_scores_accuracy                           20.51
review_scores_cleanliness                        20.50
review_scores_checkin                            20.52
review_scores_communication                      20.50
review_scores_location                           20.52
review_scores_value                              20.52
license                                         100.00
instant_bookable                                  0.00
calculated_host_listings_count                    0.00
calculated_host_listings_count_entire_homes       0.00
calculated_host_listings_count_private_rooms      0.00
calculated_host_listings_count_shared_rooms       0.00
reviews_per_month                                19.45
dtype: float64


# Identify duplicates
data.duplicated().sum()

0


# Check column names
data.columns

Index(['id', 'listing_url', 'scrape_id', 'last_scraped', 'source', 'name',
       'description', 'neighborhood_overview', 'picture_url', 'host_id',
       'host_url', 'host_name', 'host_since', 'host_location', 'host_about',
       'host_response_time', 'host_response_rate', 'host_acceptance_rate',
       'host_is_superhost', 'host_thumbnail_url', 'host_picture_url',
       'host_neighbourhood', 'host_listings_count',
       'host_total_listings_count', 'host_verifications',
       'host_has_profile_pic', 'host_identity_verified', 'neighbourhood',
       'neighbourhood_cleansed', 'neighbourhood_group_cleansed', 'latitude',
       'longitude', 'property_type', 'room_type', 'accommodates', 'bathrooms',
       'bathrooms_text', 'bedrooms', 'beds', 'amenities', 'price',
       'minimum_nights', 'maximum_nights', 'minimum_minimum_nights',
       'maximum_minimum_nights', 'minimum_maximum_nights',
       'maximum_maximum_nights', 'minimum_nights_avg_ntm',
       'maximum_nights_avg_ntm', 'calendar_updated', 'has_availability',
       'availability_30', 'availability_60', 'availability_90',
       'availability_365', 'calendar_last_scraped', 'number_of_reviews',
       'number_of_reviews_ltm', 'number_of_reviews_l30d', 'first_review',
       'last_review', 'review_scores_rating', 'review_scores_accuracy',
       'review_scores_cleanliness', 'review_scores_checkin',
       'review_scores_communication', 'review_scores_location',
       'review_scores_value', 'license', 'instant_bookable',
       'calculated_host_listings_count',
       'calculated_host_listings_count_entire_homes',
       'calculated_host_listings_count_private_rooms',
       'calculated_host_listings_count_shared_rooms', 'reviews_per_month'],
      dtype='object')


# Drop irrelevant columns (making a DataFrame backup!)
data_v2 = data[[
       #      'id', 
       'listing_url',
       #      'scrape_id', 'last_scraped', 'source', 
       'name',
       #      'description', 'neighborhood_overview', 'picture_url', 
       'host_id',
       #      'host_url', 'host_name', 
       #      'host_since', 'host_location', 'host_about',
       #      'host_response_time',
       #      'host_response_rate', 'host_acceptance_rate',
       #      'host_is_superhost', 
       #      'host_thumbnail_url', 'host_picture_url', 'host_neighbourhood', 
       #      'host_listings_count', 'host_total_listings_count', 
       #      'host_verifications',
       #      'host_has_profile_pic', 'host_identity_verified', 'neighbourhood',
       'neighbourhood_cleansed', 
       #      'neighbourhood_group_cleansed', 
       'latitude', 'longitude', 
       #      'property_type', 
       'room_type', 'accommodates', 
       #      'bathrooms',
       'bathrooms_text', 'bedrooms', 'beds', 'amenities', 'price',
       'minimum_nights',
       #      'maximum_nights', 'minimum_minimum_nights',
       #      'maximum_minimum_nights', 'minimum_maximum_nights',
       #      'maximum_maximum_nights', 'minimum_nights_avg_ntm',
       #      'maximum_nights_avg_ntm', 'calendar_updated', 'has_availability',
       #      'availability_30', 'availability_60', 'availability_90',
       #      'availability_365', 'calendar_last_scraped', 
       'number_of_reviews',
       #      'number_of_reviews_ltm', 'number_of_reviews_l30d', 
       #      'first_review', 'last_review', 'review_scores_rating', 
       #      'review_scores_accuracy',
       #      'review_scores_cleanliness', 'review_scores_checkin',
       #      'review_scores_communication', 'review_scores_location',
       #      'review_scores_value', 
       #      'license', 'instant_bookable', 'calculated_host_listings_count',
       #      'calculated_host_listings_count_entire_homes',
       #      'calculated_host_listings_count_private_rooms',
       #      'calculated_host_listings_count_shared_rooms', 
       #      'reviews_per_month'
       ]]


# Check new DataFrame dimension
data_v2.shape

(23185, 15)


# Preview the first 3 rows of the new DataFrame
data_v2.head(3)


# Check for missing values
data_v2.isnull().sum()

listing_url                  0
name                         0
host_id                      0
neighbourhood_cleansed       0
latitude                     0
longitude                    0
room_type                    0
accommodates                 0
bathrooms_text              11
bedrooms                  6297
beds                       183
amenities                    0
price                        0
minimum_nights               0
number_of_reviews            0
dtype: int64


# Check "bathrooms_text" column missing values
data_v2[data_v2['bathrooms_text'].isnull()]


# Fill all the 11 "bathrooms_text" missing values
data_v2.loc[data_v2['bathrooms_text'].isnull(), 'bathrooms_text'] = '0 baths'


# Check "bathrooms_text" missing values
data_v2['bathrooms_text'].isnull().sum()

0


# Check "bedrooms" column empty values
data_v2.loc[data_v2['bedrooms'].isnull()].head(10)


# Extract values from the column "name" to fill missing values in the "bedrooms" column
bedrooms_info = data_v2[data_v2['bedrooms'].isnull()]['name'].str.split('·', expand=True).applymap(lambda x: x.strip() if x is not None else x).copy()


# Preview the first 10 rows
bedrooms_info.head(10)


# Check the values from the second column to extract the bedroom information
bedrooms_info[1].value_counts()

1 bedroom     2121
★5.0           727
★New           336
2 bedrooms     226
Studio         200
★4.67          121
★4.75          101
★4.80           93
★4.83           87
★4.50           84
★4.86           80
★4.91           78
★4.94           76
★4.90           74
★4.95           72
★4.0            71
★4.89           70
★4.88           66
★4.93           65
★4.33           65
★4.92           62
★4.96           60
★4.78           59
★4.85           59
3 bedrooms      56
★4.97           50
★4.82           42
★4.71           40
★4.87           38
★4.60           38
★4.25           38
★4.79           36
★4.69           33
★4.77           32
★4.98           30
★4.81           30
★4.56           28
★4.84           26
★4.38           26
★4.64           25
★4.76           24
★4.72           23
★4.63           23
★4.73           22
4 bedrooms      21
★4.74           19
★4.70           19
★4.68           18
★4.58           17
★4.40           17
★4.65           17
★4.62           16
★4.57           16
★4.44           16
★4.55           15
★4.53           14
★4.54           14
★4.43           13
★3.67           13
★4.61           13
★4.47           13
★3.33           11
★3.50           11
★4.59           11
★4.20           11
★4.42           11
★4.36           11
★4.45           10
★4.46           10
★3.75            9
★4.27            8
★3.83            8
★4.17            7
★4.37            7
★4.29            7
★3.0             7
★4.66            7
★4.41            7
★4.52            7
★4.14            7
★3.60            6
★4.13            6
★4.31            6
★4.22            5
★3.80            5
★4.99            5
★4.09            5
★4.11            5
★4.30            5
★4.35            5
★4.39            4
★0.0             4
★4.26            4
★4.16            4
1 bed            4
★4.10            4
★3.86            3
6 bedrooms       3
★4.08            3
★4.49            3
★4.51            3
★4.24            3
★4.15            3
★4.28            3
5 bedrooms       3
★4.04            2
★3.89            2
★4.23            2
★4.21            2
★3.57            2
★4.34            2
★2.33            2
★4.19            2
★4.32            2
★4.06            2
★2.67            2
★2.0             1
★3.68            1
★3.20            1
★4.48            1
★3.79            1
★3.91            1
★2.80            1
3 beds           1
★3.85            1
★2.20            1
★3.92            1
7 bedrooms       1
★3.94            1
★2.75            1
★3.25            1
★4.07            1
★3.40            1
★3.93            1
2 beds           1
★3.90            1
Name: 1, dtype: int64


# Create a list to extract the bedroom information from the second column in bedrooms_info
specific_values_1 = ['Studio', '1 bedroom', '2 bedrooms', '3 bedrooms', 
                    '4 bedrooms', '5 bedrooms', '6 bedrooms',
                    '7 bedrooms']


# Check the values from the third column to extract the bedroom information
bedrooms_info[2].value_counts()

1 bedroom           2801
1 bed               2158
Studio               529
2 beds               309
2 bedrooms           215
3 beds                63
3 bedrooms            57
4 beds                33
1 shared bath         29
4 bedrooms            25
1 bath                15
1 private bath        15
5 beds                12
6 bedrooms             7
5 bedrooms             6
2 baths                3
1.5 shared baths       3
14 bedrooms            3
0 shared baths         2
1.5 baths              2
2 shared baths         2
2.5 shared baths       2
8 beds                 1
7 bedrooms             1
0 baths                1
6 beds                 1
7 beds                 1
Name: 2, dtype: int64


# Create a list to extract the bedroom information from the third column in bedrooms_info
specific_values_2 = ['Studio', '1 bedroom', '2 bedrooms', '3 bedrooms', 
                    '4 bedrooms', '5 bedrooms', '6 bedrooms',
                    '7 bedrooms', '14 bedrooms']


# Extract values from the second column
bedrooms_info_1 = bedrooms_info[bedrooms_info[1].isin(specific_values_1)]


# Extract values from the third column
bedrooms_info_2 = bedrooms_info[bedrooms_info[2].isin(specific_values_2)]


# Concat bedroom information from the second and third columns
bedrooms_info_complete = pd.concat([bedrooms_info_1[1], bedrooms_info_2[2]],)


# Sort the index
bedrooms_info_complete.sort_index(inplace=True)


# Check unique value
bedrooms_info_complete.value_counts()

1 bedroom      4922
Studio          729
2 bedrooms      441
3 bedrooms      113
4 bedrooms       46
6 bedrooms       10
5 bedrooms        9
14 bedrooms       3
7 bedrooms        2
dtype: int64


# Convert string values to numeric values
replace_values = {'Studio': 0.0, '1 bedroom': 1.0, '2 bedrooms': 2.0, '3 bedrooms': 3.0, '4 bedrooms': 4.0, '5 bedrooms': 5.0, '6 bedrooms': 6.0, '7 bedrooms': 7.0, '14 bedrooms': 14.0}
bedrooms_info_complete = bedrooms_info_complete.replace(replace_values)


# Check unique values 
bedrooms_info_complete.value_counts()

1.0     4922
0.0      729
2.0      441
3.0      113
4.0       46
6.0       10
5.0        9
14.0       3
7.0        2
dtype: int64


# Make another DataFrame backup
data_v3 = data_v2.copy()


# Fill the "bedrooms" missing values with bedroom information
data_v3['bedrooms'] = data_v3['bedrooms'].fillna(bedrooms_info_complete)


# Check "bedrooms" missing values
data_v3['bedrooms'].isnull().sum()

22


# Check the "bedrooms" 22 missing values
data_v3.loc[data_v3['bedrooms'].isnull()].head(22)


# Fill in the missing values in the 'bedrooms' column with 0.0, which represents 'Studio'
data_v3[data_v3['bedrooms'].isnull()] = data_v3[data_v3['bedrooms'].isnull()].fillna(0.0)


# Check for missing values
data_v3.isnull().sum()

listing_url                 0
name                        0
host_id                     0
neighbourhood_cleansed      0
latitude                    0
longitude                   0
room_type                   0
accommodates                0
bathrooms_text              0
bedrooms                    0
beds                      183
amenities                   0
price                       0
minimum_nights              0
number_of_reviews           0
dtype: int64


# Check "beds" column missing values
data_v3[data_v3['beds'].isnull()].head()


# Impute missing 'beds' values for listings accommodating up to 2 guests in 1 bedroom or less with the mode of 'beds', which is 1
data_v3.loc[(data_v3['beds'].isnull()) & (data_v3['accommodates'] <= 2) & (data_v3['bedrooms'] <= 1), 'beds'] = st.mode(data_v3['beds'])


# Check for missing values
data_v3.isnull().sum()

listing_url                0
name                       0
host_id                    0
neighbourhood_cleansed     0
latitude                   0
longitude                  0
room_type                  0
accommodates               0
bathrooms_text             0
bedrooms                   0
beds                      31
amenities                  0
price                      0
minimum_nights             0
number_of_reviews          0
dtype: int64


# Analyze the missing 'beds' values and checking the 'listing_url' column as a reference to handle the missing values
data_v3[data_v3['beds'].isnull()].head(10)


# Fill the 'beds' missing values
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/5573248', 'beds'] = 4.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/5096866', 'beds'] = 1.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/13592574', 'beds'] = 1.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/14094124', 'beds'] = 2.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/16440438', 'beds'] = 2.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/16233947', 'beds'] = 3.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/23931198', 'beds'] = 3.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/33601421', 'beds'] = 2.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/38472816', 'beds'] = 1.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/39319156', 'beds'] = 9.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/39454193', 'beds'] = 1.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/40165294', 'beds'] = 1.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/40603921', 'beds'] = 1.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/42317481', 'beds'] = 1.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/42318082', 'beds'] = 2.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/43293837', 'beds'] = 1.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/43117636', 'beds'] = 2.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/46879468', 'beds'] = 2.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/49820299', 'beds'] = 1.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/582835905208247854', 'beds'] = 3.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/720080948267459603', 'beds'] = 2.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/799591397619258644', 'beds'] = 2.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/798136718669520279', 'beds'] = 2.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/840129849005783804', 'beds'] = 3.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/939039430629377525', 'beds'] = 1.0
data_v3.loc[data_v3['listing_url'] == 'https://www.airbnb.com/rooms/962738485338707503', 'beds'] = 2.0


# Drop 5 rows from the 'beds' column
data_v3 = data_v3.dropna(subset=['beds']).reset_index(drop=True)


# Check for missing values
round(data_v3.isnull().sum())

listing_url               0
name                      0
host_id                   0
neighbourhood_cleansed    0
latitude                  0
longitude                 0
room_type                 0
accommodates              0
bathrooms_text            0
bedrooms                  0
beds                      0
amenities                 0
price                     0
minimum_nights            0
number_of_reviews         0
dtype: int64


# Check the percentage of missing values
round(data_v3.isnull().sum() / len(data_v3) * 100,2)

listing_url               0.0
name                      0.0
host_id                   0.0
neighbourhood_cleansed    0.0
latitude                  0.0
longitude                 0.0
room_type                 0.0
accommodates              0.0
bathrooms_text            0.0
bedrooms                  0.0
beds                      0.0
amenities                 0.0
price                     0.0
minimum_nights            0.0
number_of_reviews         0.0
dtype: float64


# Check data type
data_v3.dtypes

listing_url                object
name                       object
host_id                     int64
neighbourhood_cleansed     object
latitude                  float64
longitude                 float64
room_type                  object
accommodates                int64
bathrooms_text             object
bedrooms                  float64
beds                      float64
amenities                  object
price                      object
minimum_nights              int64
number_of_reviews           int64
dtype: object


# Convert the column 'price' to numeric
data_v3['price'] = pd.to_numeric(data_v3['price'].str.replace(r'[\$,]', '', regex=True))


# Check 'price' data type
data_v3['price'].dtypes

dtype('float64')


# Convert the columns 'bedrooms' and 'beds' to integers
data_v3[['bedrooms', 'beds']] = data_v3[['bedrooms', 'beds']].astype('int64')


# Check 'bedrooms' and 'beds' data type
data_v3[['bedrooms', 'beds']].dtypes

bedrooms    int64
beds        int64
dtype: object


# Check unique values in 'neighbourhood_cleansed' column
data_v3['neighbourhood_cleansed'].value_counts()

Melbourne            7279
Port Phillip         2742
Yarra                1776
Stonnington          1514
Yarra Ranges         1112
Moreland              942
Monash                590
Boroondara            589
Whitehorse            583
Wyndham               576
Glen Eira             558
Darebin               551
Maribyrnong           515
Bayside               401
Kingston              363
Moonee Valley         353
Hobsons Bay           267
Frankston             263
Brimbank              258
Casey                 234
Manningham            227
Hume                  207
Banyule               201
Cardinia              186
Knox                  172
Greater Dandenong     172
Whittlesea            172
Melton                157
Maroondah             117
Nillumbik             103
Name: neighbourhood_cleansed, dtype: int64


# Replace 'Moreland' values in the 'neighbourhood_cleansed' column with 'Merri-bek'
data_v3.loc[data_v3['neighbourhood_cleansed'] == 'Moreland', 'neighbourhood_cleansed'] = 'Merri-bek'


# Check duplicate data
data_v3.duplicated().sum()

0


# Check quantitative data
data_v3.describe()


# Create the graph 
fig = px.box(data_v3, x='beds')
fig.update_layout(
    title='Boxplot analysis variable - beds',
    title_x=0.5,
    autosize=False,
    width=800,
    height=500)

# Show the boxplot
fig.show()


# Inspect the 'beds' column where values are higher or equal to 22 using the 'listing_url' to check the 'beds' values
data_v3[data_v3['beds'] >= 22].head(3)


# Filter 'beds' column with values above or equal to 7 and counting them
data_v3[data_v3['beds'] >= 7].shape

(463, 15)


# Remove the outlier from the 'beds' column and reseting the DataFrame index
data_v3 = data_v3[data_v3['beds'] != 117].reset_index(drop=True)


# Create the graph
fig = px.box(data_v3, x='beds')
fig.update_layout(
    title='Boxplot analysis variable - beds',
    title_x=0.5,
    autosize=False,
    width=800,
    height=500)

# Show the boxplot
fig.show()


# Check quantitative data
data_v3.describe()


# Filter 'price' column with values below $30
data_v3[(data_v3['price'] < 30)].head(15)


# Replace the values below 30 in the 'price' column with the median of the 'price' column
data_v3.loc[data_v3['price'] < 30, 'price'] = data_v3['price'].median()


# Create the graph 
fig = px.box(data_v3, x='price')
fig.update_layout(
    title='Boxplot analysis variable - price',
    title_x=0.5,
    autosize=False,
    width=800,
    height=500)

# Show the boxplot
fig.show()


# Filter prices higher than $2000
data_v3[(data_v3['price'] > 2000)].head(10)


# Remove values above to 2000
data_v3 = data_v3[data_v3['price'] <= 2000].reset_index(drop=True)


# Create the graph 
fig = px.box(data_v3, x='price')
fig.update_layout(
    title='Boxplot analysis variable - price',
    title_x=0.5,
    autosize=False,
    width=800,
    height=500
)

# Show the boxplot
fig.show()


# Check quantitative data
data_v3.describe()


# Create the graph
fig = px.box(data_v3, x='minimum_nights')
fig.update_layout(
    title='Boxplot analysis variable - minimum_nights',
    title_x=0.5,
    autosize=False,
    width=800,
    height=500
)

# Show the boxplot
fig.show()


# Filter 'minimum_nights' column with values above 365
data_v3[data_v3['minimum_nights'] > 365].head(10)


# Update the 'minimum_nights' value equal to 999 with 2, removing the values above 365 and reseting the DataFrame index
data_v3.loc[(data_v3['minimum_nights'] == 999), 'minimum_nights'] = 2
data_v3 = data_v3.loc[(data_v3['minimum_nights'] <= 365)].reset_index(drop=True)


# Create the graph
fig = px.box(data_v3, x='minimum_nights')
fig.update_layout(
    title='Boxplot analysis variable - minimum_nights',
    title_x=0.5,
    autosize=False,
    width=800,
    height=500
)

# Show the boxplot
fig.show()


# Drop the 'name' and 'listing_url' columns
data_v3 = data_v3.drop(columns=['name', 'listing_url'])


# Make DataFrame backup
data_v4 = data_v3.copy()


# Export DataFrame
data_v4.to_csv('listings_cleaned.csv', index=False)


# Create a frequency table for the top 10 hosts
top_10_hosts = pd.DataFrame({
    'Host ID': data_v4['host_id'].value_counts().head(10).index,
    'Number of Listings': data_v4['host_id'].value_counts().head(10).values,
    'Percentage of Listings': np.round((data_v4['host_id'].value_counts().head(10) / data_v4.shape[0]) * 100, 2)
})

# Reset the index to get a standard numeric index
top_10_hosts.reset_index(drop=True, inplace=True)

# Adjust the index to start at 1 instead of 0
top_10_hosts.index = top_10_hosts.index + 1

# Display the frequency table
top_10_hosts


# Filter the top 10 hosts
top_10_hosts = data_v4['host_id'].astype(str).value_counts().head(10).sort_values()

# Create the graph
fig = go.Figure(data=[go.Bar(
    x=top_10_hosts.values, y=top_10_hosts.index,
    orientation='h', text=top_10_hosts.values, textposition='auto',
    hovertemplate='Host ID: %{y}<br>Number of Listings: %{x}<extra></extra>', 
    marker_color='#f5b7b9'
)])

# Customize the layout
fig.update_layout(
    autosize=False, title='<b>Top 10 Hosts Listings</b>', title_x=0.5,
    width=800, height=500,
    hoverlabel=dict(bgcolor="white", font_size=16, font_family="Rockwell")
)

# Add the source
fig.add_annotation(
    text='<b>Source: Inside Airbnb</b>', align='right', showarrow=False, 
    xref='paper', yref='paper', x=1, y=-0.2, 
    bgcolor='white', font=dict(size=12, color='black')
)

# Show the chart
fig.show()


# Create a frequency table for the top 10 neighbourhoods
top_10_neighbourhoods = pd.DataFrame({
    'Neighbourhood': data_v4['neighbourhood_cleansed'].value_counts().head(10).index,
    'Number of Listings': data_v4['neighbourhood_cleansed'].value_counts().head(10).values,
    'Percentage of Listings': np.round((data_v4['neighbourhood_cleansed'].value_counts().head(10) / data_v4.shape[0]) * 100, 2)
})

# Reset the index to get a standard numeric index
top_10_neighbourhoods.reset_index(drop=True, inplace=True)

# Adjust the index to start at 1 instead of 0
top_10_neighbourhoods.index = top_10_neighbourhoods.index + 1

# Display the frequency table
top_10_neighbourhoods


# Filter the top 10 neighbourhoods
top_10_neighbourhoods = data_v4['neighbourhood_cleansed'].value_counts().head(10).sort_values()

# Create the graph
fig = go.Figure(data=[go.Bar(
    x=top_10_neighbourhoods.values, y=top_10_neighbourhoods.index,
    orientation='h', text=top_10_neighbourhoods.values, textposition='auto',
    hovertemplate='Neighbourhood: %{y}<br>Number of Listings: %{x}<extra></extra>', 
    marker_color='#f5b7b9'
)])

# Customize the layout
fig.update_layout(
    autosize=False, title='<b>Top 10 Neighbourhoods with Most Listings</b>', title_x=0.5,
    width=800, height=500,
    hoverlabel=dict(bgcolor="white", font_size=16, font_family="Rockwell")
)

# Add the source
fig.add_annotation(
    text='<b>Source: Inside Airbnb</b>', align='right', showarrow=False, 
    xref='paper', yref='paper', x=1, y=-0.2, 
    bgcolor='white', font=dict(size=12, color='black')
)

# Show the chart
fig.show()


# Load the GeoJSON file
gdf = gpd.read_file('/kaggle/input/melbourne-airbnb-september-2023-dataset/neighbourhoods.geojson')

# Count the number of listings and add to the GeoDataFrame
gdf['listings'] = gdf['neighbourhood'].map(data_v4['neighbourhood_cleansed'].value_counts())

# Filter the 10 neighbourhoods with the most listings
top_10_neighbourhoods = gdf.nlargest(10, 'listings')

# Create a new column with the neighbourhood names
top_10_neighbourhoods['Neighbourhood'] = top_10_neighbourhoods['neighbourhood']

# Create the interactive map plot
fig = px.choropleth_mapbox(top_10_neighbourhoods, geojson=gdf.geometry, 
                           locations=top_10_neighbourhoods.index, color='listings',
                           hover_data={'Neighbourhood':True, 'listings':True},
                           color_continuous_scale=[(0, '#FFFFFF'), (1, '#ff4c5e')], # Custom colorscale
                           mapbox_style="open-street-map",
                           zoom=10, center = {"lat": -37.8136, "lon": 144.9631}, opacity=0.5,
                           labels={'listings':'Number of Listings'})

# Customize the layout
fig.update_layout(
    autosize=False, width=800, height=500, margin={"r":0,"t":0,"l":0,"b":0},
    hoverlabel=dict(bgcolor="white", font_size=16, font_family="Rockwell"
))

# Update the hover template
fig.update_traces(hovertemplate='<b>Neighbourhood: %{customdata[0]}</b><br><b>Number of Listings: %{z}</b>')

# Show the chart
fig.show()


# Create a frequency table for room types
room_type = pd.DataFrame({
    'Room Type': data_v4['room_type'].value_counts().head().index,
    'Number of Listings': data_v4['room_type'].value_counts().head().values,
    'Percentage of Listings': np.round((data_v4['room_type'].value_counts().head() / data_v4.shape[0]) * 100, 2)
})

# Reset the index to get a standard numeric index
room_type.reset_index(drop=True, inplace=True)

# Adjust the index to start at 1 instead of 0
room_type.index = room_type.index + 1

# Display the frequency table
room_type


# Filter room types and calculate their percentage
room_type = (data_v4['room_type'].value_counts() / data_v4.shape[0] * 100).round(2)

# Create the graph
fig = go.Figure(data=[go.Bar(
    x=room_type.index, y=room_type.values, 
    text=room_type.values, textposition='auto',
    hovertemplate='Room type: %{x}<br>Number of Listings: %{text}%<extra></extra>', 
    marker_color='#f5b7b9'
)])

# Customize the layout
fig.update_layout(
    autosize=False, title='<b>Room Type Distribution</b>', title_x=0.5,
    width=800, height=500,
    hoverlabel=dict(bgcolor="white", font_size=16, font_family="Rockwell")
)

# Add the source
fig.add_annotation(
    text='<b>Source: Inside Airbnb</b>', align='right', showarrow=False, 
    xref='paper', yref='paper', x=1, y=-0.2, 
    bgcolor='white', font=dict(size=12, color='black')
)

# Show the chart
fig.show()


# Create a frequency table for the top 5 bedroom counts
top_5_bedrooms = pd.DataFrame({
    'Number of Bedroom': data_v4['bedrooms'].value_counts().head().index,
    'Number of Listings': data_v4['bedrooms'].value_counts().head().values,
    'Percentage of Listings': np.round((data_v4['bedrooms'].value_counts().head() / data_v4.shape[0]) * 100, 2)
})

# Reset the index to get a standard numeric index
top_5_bedrooms.reset_index(drop=True, inplace=True)

# Adjust the index to start at 1 instead of 0
top_5_bedrooms.index = top_5_bedrooms.index + 1

# Display the frequency table
top_5_bedrooms


# Filter bedrooms booked and calculate their percentage
bedrooms_booked = (data_v4['bedrooms'].value_counts().head() / data_v4.shape[0] * 100).round(2)

# Create the graph
fig = go.Figure(data=[go.Bar(
    x=bedrooms_booked.index.astype(str), y=bedrooms_booked.values, 
    text=bedrooms_booked.values, textposition='auto',
    hovertemplate='Number of Bedroom: %{x}<br>Number of Listings: %{text}%<extra></extra>', 
    marker_color='#f5b7b9'
)])

# Customize the layout
fig.update_layout(
    autosize=False, title='<b>The 5 Most Common Bedroom</b>', title_x=0.5,
    width=800, height=500,
    hoverlabel=dict(bgcolor="white", font_size=16, font_family="Rockwell")
)

# Add an annotation for clarity
fig.add_annotation(
    text='<b>Note: Bedroom = 0 (Studio)</b>', align='right', showarrow=False, 
    xref='paper', yref='paper',
    x=1, y=-0.14, 
    bgcolor='white', font=dict(size=12, color='black')
)

# Add the source
fig.add_annotation(
    text='<b>Source: Inside Airbnb</b>', align='right', showarrow=False, 
    xref='paper', yref='paper', x=1, y=-0.2, 
    bgcolor='white', font=dict(size=12, color='black')
)

# Show the chart
fig.show()


# Create a frequency table for the most common minimum nights
most_minimum_nights = pd.DataFrame({
    'Minimum Nights': data_v4['minimum_nights'].value_counts().head().index,
    'Number of Listings': data_v4['minimum_nights'].value_counts().head().values,
    'Percentage of Listings': np.round((data_v4['minimum_nights'].value_counts().head() / data_v4.shape[0]) * 100, 2)
})

# Reset the index to get a standard numeric index
most_minimum_nights.reset_index(drop=True, inplace=True)

# Adjust the index to start at 1 instead of 0
most_minimum_nights.index = most_minimum_nights.index + 1

# Display the frequency table
most_minimum_nights


# Create a frequency table for the most common minimum nights
most_minimum_nights = pd.DataFrame({
    'Minimum Nights': data_v4['minimum_nights'].value_counts().head().index,
    'Number of Listings': data_v4['minimum_nights'].value_counts().head().values,
    'Percentage of Listings': np.round((data_v4['minimum_nights'].value_counts().head() / data_v4.shape[0]) * 100, 2)
})

# Reset the index to get a standard numeric index
most_minimum_nights.reset_index(drop=True, inplace=True)

# Adjust the index to start at 1 instead of 0
most_minimum_nights.index = most_minimum_nights.index + 1

# Create the graph
fig = go.Figure(data=[go.Bar(
    x=most_minimum_nights['Minimum Nights'].astype(str), y=most_minimum_nights['Number of Listings'], 
    text=most_minimum_nights['Number of Listings'], textposition='auto',
    hovertemplate='Minimum Nights: %{x}<br>Number of Listings: %{y}<extra></extra>', 
    marker_color='#f5b7b9'
)])

# Customize the layout
fig.update_layout(
    autosize=False, title='<b>The 5 Most Minimum Nights</b>', title_x=0.5,
    width=800, height=500,
    hoverlabel=dict(bgcolor="white", font_size=16, font_family="Rockwell")
)

# Add the source
fig.add_annotation(
    text='<b>Source: Inside Airbnb</b>', align='right', showarrow=False, 
    xref='paper', yref='paper', x=1, y=-0.2, 
    bgcolor='white', font=dict(size=12, color='black')
)

# Show the chart
fig.show()


# Transform the string series into a list series and counting the total of each amenity
amenities = [item for sublist in data_v4['amenities'].apply(ast.literal_eval) for item in sublist]
quantity_amenities = pd.Series(amenities).value_counts().head(4)

# Create the frequency table
quantity_amenities = pd.DataFrame({
        'Amenity': quantity_amenities.index,
        'Number of Amenities': quantity_amenities.values,
        'Percentage of Amenities': np.round((quantity_amenities.values / data_v4.shape[0]) * 100, 2)
})

# Reset the index to get a standard numeric index
quantity_amenities.reset_index(drop=True, inplace=True)

# Adjust the index to start at 1
quantity_amenities.index = quantity_amenities.index + 1

# Display the frequency table
quantity_amenities


# Sort the DataFrame in ascending order by 'Number of Amenities'
quantity_amenities = quantity_amenities.sort_values('Number of Amenities', ascending=True)

# Create the bar chart
fig = go.Figure(go.Bar(
    x=quantity_amenities['Number of Amenities'], y=quantity_amenities['Amenity'], orientation='h', 
    text=quantity_amenities['Number of Amenities'], textposition='auto',
    hovertemplate='Amenities: %{y}<br>Number of Amenities: %{x}<extra></extra>', marker_color='#f5b7b9'
))

# Customize the layout
fig.update_layout(
    autosize=False, title_text='<b>4 Amenities Most Commonly Provided</b>', title_x=0.5,
    width=800, height=500, hoverlabel=dict(bgcolor="white", font_size=16, font_family="Rockwell"
))

# Add the source
fig.add_annotation(
    text='<b>Source: Inside Airbnb</b>', align='right', showarrow=False, xref='paper', yref='paper',
    x=1, y=-0.2, bgcolor='white', font=dict(size=12, color='black'
))

# Show the chart
fig.show()


# Create a frequency table for room types and their reviews
room_type_reviews = pd.DataFrame({
    'Room Type': data_v4['room_type'].value_counts().index,
    'Number of Reviews': data_v4.groupby('room_type')['number_of_reviews'].sum(),
    'Percentage of Reviews': np.round(data_v4.groupby('room_type')['number_of_reviews'].sum() / data_v4['number_of_reviews'].sum() * 100, 2)
})

# Sort the values in the frequency table by 'Number of Reviews'
room_type_reviews = room_type_reviews.nlargest(4, 'Number of Reviews').reset_index(drop=True)

# Display the frequency table
room_type_reviews


# Create the graph
fig = go.Figure(data=[go.Bar(x=room_type_reviews['Room Type'], y=room_type_reviews['Number of Reviews'],
                             text=room_type_reviews['Number of Reviews'], textposition='auto',
                             hovertemplate='Room Type: %{x}<br>Number of Reviews: %{y}<extra></extra>', marker_color='#f5b7b9'
)])

# Customize the layout
fig.update_layout(title='<b>Room Type with the Highest Reviews</b>', title_x=0.5,
                   width=800, height=500,
                   hoverlabel=dict(bgcolor="white", font_size=16, font_family="Rockwell"
))

# Add the source
fig.add_annotation(text='<b>Source: Inside Airbnb</b>', align='right', showarrow=False, xref='paper', yref='paper',
                    x=1, y=-0.2, bgcolor='white', font=dict(size=12, color='black'
))

# Show the chart
fig.show()


# Group by room type and calculating the average number of amenities
room_type_amenities = data_v4['room_type'].value_counts().index.to_series().map(lambda x: data_v4[data_v4['room_type'] == x]['amenities'].apply(lambda y: len(ast.literal_eval(y))).mean())

# Create a DataFrame
room_type_amenities = pd.DataFrame({'Room Type': room_type_amenities.index, 
                                    'Average Amenities': room_type_amenities.values}).round(2).sort_values('Average Amenities', ascending=False)

# Reset the index to start from 1
room_type_amenities.reset_index(drop=True, inplace=True)

# Adjust the index to start at 1
room_type_amenities.index = room_type_amenities.index + 1

# Display the DataFrame
room_type_amenities


# Create the graph
fig = go.Figure(data=[go.Bar(
    x=room_type_amenities['Room Type'], y=room_type_amenities['Average Amenities'],
    text=room_type_amenities['Average Amenities'], textposition='auto', hovertemplate='Room Type: %{x}<br>Average Amenities: %{y}<extra></extra>',
    marker_color='#f5b7b9'
)])

# Customize the layout
fig.update_layout(
    title='<b>Average Amenities by Room Type</b>', title_x=0.5, width=800, height=500,
    hoverlabel=dict(bgcolor="white", font_size=16, font_family="Rockwell"
))

# Add the source
fig.add_annotation(
    text='<b>Source: Inside Airbnb</b>', align='right', showarrow=False, xref='paper', yref='paper', x=1, y=-0.2,
    bgcolor='white', font=dict(size=12, color='black'
))

# Show the chart
fig.show()


# Create a table with the average for each room type
average_room_type = pd.DataFrame(
    round(data_v4[data_v4['room_type'].isin(data_v4['room_type'].value_counts().head(5).index)]
          .groupby('room_type')['price'].mean(),2)).reset_index().rename(columns={'room_type': 'Room Type', 'price': 'Average Price'}).sort_values('Average Price', ascending=True)

# Reset the index to start from 1
average_room_type = average_room_type.reset_index(drop=True)

# Adjust the index to start at 1
average_room_type.index = average_room_type.index + 1

# Show the table
average_room_type


# Create the graph
fig = go.Figure(data=[go.Bar(
    x=average_room_type['Room Type'], y=average_room_type['Average Price'],
    text=average_room_type['Average Price'], textposition='auto',
    hovertemplate='Room Type: %{x}<br>Average Price: %{y}<extra></extra>', marker_color='#f5b7b9'
)])

# Customize the layout
fig.update_layout(
    title='<b>Average Prices for each Room Type</b>', title_x=0.5,
    width=800, height=500,
    hoverlabel=dict(bgcolor="white", font_size=16, font_family="Rockwell"
))

# Add the source
fig.add_annotation(
    text='<b>Source: Inside Airbnb</b>', align='right', showarrow=False, xref='paper', yref='paper',
    x=1, y=-0.2, bgcolor='white', font=dict(size=12, color='black'
))

# Show the chart
fig.show()


# Select quantitative variables (without latitude and longitude)
quantitative_variables = data_v4.select_dtypes(include=['int64', 'float64'])
quantitative_variables = quantitative_variables.iloc[:, 3:]

# Preview the 3 rows
quantitative_variables.head(3)


# Calculate the Pearson coeficient
correlation_matrix = quantitative_variables.corr(method='pearson')

# Exhibit correlation_matriz
correlation_matrix


# Get correlation matrix
z = correlation_matrix.values.round(2)

# Get variable names
x = correlation_matrix.columns
y = correlation_matrix.index

# Create the heatmap
heatmap = go.Figure(data=go.Heatmap(
                   z=z, x=x, y=y, colorscale=[(0, '#FFFFFE'), (1, '#ff4c5e')],
                   hoverongaps = False, hoverinfo='z'
))

# Customize the layout
heatmap.update_layout(
    title='<b>Correlation Matrix</b>', title_x=0.5,
    width=800, height=500,
    hoverlabel=dict(font_size=16, font_family="Rockwell"
))

# Add the source
heatmap.add_annotation(
    text='<b>Source: Inside Airbnb</b>', align='right', showarrow=False, xref='paper', yref='paper',
    x=1, y=-0.2, bgcolor='white', font=dict(size=12, color='black'
))

# Add annotations (values) to each cell
for i in range(len(y)):
    for j in range(len(x)):
        # Round the value to 2 decimal places
        value = round(z[i][j], 2)
        heatmap.add_annotation(dict(
            x=x[j], y=y[i], text=str(value),
            showarrow=False, font=dict(size=12)
))

# Show the plot
heatmap.show()


# Calculate the correlation
correlation_price_review = data_v4[['price', 'number_of_reviews']].corr().round(2)

# Display the correlation matrix
correlation_price_review


# Create a scatter plot of 'number_of_reviews' vs 'price' using Plotly graph objects
scatter = go.Scatter(
    x=data_v4['number_of_reviews'], y=data_v4['price'], mode='markers', 
    hovertemplate='Number of Reviews: %{x}<br>Price: %{y}<extra></extra>', marker_color='#f5b7b9'
)

# Customize the layout
layout = go.Layout(
    title='<b>Price vs Number of Reviews</b>', title_x=0.5, 
    xaxis_title='<b>Number of Reviews</b>', yaxis_title='<b>Price</b>',
    width=800, height=500,
    hoverlabel=dict(bgcolor='white', font_size=16, font_family="Rockwell"
))

# Create the figure and adding the scatter plot
fig = go.Figure(data=scatter, layout=layout)

# Add the source
fig.add_annotation(
    text='<b>Source: Inside Airbnb</b>', align='right', showarrow=False, xref='paper', yref='paper',
    x=1, y=-0.2, bgcolor='white', font=dict(size=12, color='black'
))

# Show the chart
fig.show()


# Calculate the number of amenities for each listing
num_amenities = data_v4['amenities'].apply(lambda x: len(x))

# Check the correlation using the .corr() method, using as argument the Pearson method
pd.concat([num_amenities, data_v4['price']], axis=1).corr(method='pearson').round(2)


# Create a scatterplot to visualize the relationship using Plotly's graph_objects
fig = go.Figure(data=go.Scatter(
    x=num_amenities, y=data_v4['price'], mode='markers', 
    hovertemplate='Number of Amenities: %{x}<br>Price: %{y}<extra></extra>', marker_color='#f5b7b9'
))

# Customize the layout
fig.update_layout(title='<b>Price vs Number of Amenities</b>', title_x=0.5, 
                  xaxis_title='<b>Number of Amenities</b>', yaxis_title='<b>Price</b>',
                  width=800, height=500,
                  hoverlabel=dict(bgcolor='white', font_size=16, font_family="Rockwell"
))

# Add the source
fig.add_annotation(
    text='<b>Source: Inside Airbnb</b>', align='right', showarrow=False, xref='paper', yref='paper',
    x=1, y=-0.2, bgcolor='white', font=dict(size=12, color='black'
))

# Show the chart
fig.show()

	id	listing_url	scrape_id	last_scraped	source	name	description	neighborhood_overview	picture_url	host_id	host_url	host_name	host_since	host_location	host_about	host_response_time	host_response_rate	host_acceptance_rate	host_is_superhost	host_thumbnail_url	host_picture_url	host_neighbourhood	host_listings_count	host_total_listings_count	host_verifications	host_has_profile_pic	host_identity_verified	neighbourhood	neighbourhood_cleansed	neighbourhood_group_cleansed	latitude	longitude	property_type	room_type	accommodates	bathrooms	bathrooms_text	bedrooms	beds	amenities	price	minimum_nights	maximum_nights	minimum_minimum_nights	maximum_minimum_nights	minimum_maximum_nights	maximum_maximum_nights	minimum_nights_avg_ntm	maximum_nights_avg_ntm	calendar_updated	has_availability	availability_30	availability_60	availability_90	availability_365	calendar_last_scraped	first_review	last_review	review_scores_rating	review_scores_accuracy	review_scores_cleanliness	review_scores_checkin	review_scores_communication	review_scores_location	review_scores_value	license	instant_bookable	calculated_host_listings_count	calculated_host_listings_count_entire_homes	calculated_host_listings_count_private_rooms	reviews_per_month
23182	971613881418926837	https://www.airbnb.com/rooms/971613881418926837	20230904181306	2023-09-05	city scrape	Home in Abbotsford · ★New · 4 bedrooms · 4 bed...	Cả nhóm sẽ dễ dàng tiếp cận mọi địa điểm từ ch...	NaN	https://a0.muscache.com/pictures/hosting/Hosti...	535127590	https://www.airbnb.com/users/show/535127590	Trần	2023-09-02	Abbotsford, Australia	NaN	NaN	NaN	NaN	f	https://a0.muscache.com/im/pictures/user/User-...	https://a0.muscache.com/im/pictures/user/User-...	NaN	1.0	1.0	['email', 'phone']	t	t	NaN	Yarra	NaN	-37.807580	145.000010	Entire home	Entire home/apt	3	NaN	1 bath	4.0	4.0	["Wifi", "Kitchen", "Free parking on premises"...	$214.00	1	365	1	1	365	365	1.0	365.0	NaN	f	29	59	89	269	2023-09-05	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	f	1	1	0	NaN
23183	971616602219608699	https://www.airbnb.com/rooms/971616602219608699	20230904181306	2023-09-04	city scrape	Rental unit in Box Hill · ★New · 2 bedrooms · ...	Enjoy easy access to everything from this perf...	NaN	https://a0.muscache.com/pictures/hosting/Hosti...	163554327	https://www.airbnb.com/users/show/163554327	Steven	2017-12-21	Box Hill South, Australia	NaN	within an hour	100%	99%	f	https://a0.muscache.com/im/pictures/user/70c58...	https://a0.muscache.com/im/pictures/user/70c58...	NaN	12.0	21.0	['email', 'phone']	t	t	NaN	Whitehorse	NaN	-37.818577	145.116941	Entire rental unit	Entire home/apt	4	NaN	2 baths	2.0	3.0	["Pool", "Wifi", "Kitchen", "Hot tub", "BBQ gr...	$118.00	1	365	1	1	365	365	1.0	365.0	NaN	t	30	60	90	179	2023-09-04	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	t	11	11	0	NaN
23184	971632586652679242	https://www.airbnb.com/rooms/971632586652679242	20230904181306	2023-09-04	city scrape	Home in Springvale · ★New · Studio · 1 bed · 0...	The room is spacious with built in robe and a ...	NaN	https://a0.muscache.com/pictures/hosting/Hosti...	395737294	https://www.airbnb.com/users/show/395737294	Ratana	2021-04-06	Springvale, Australia	I am a professional engineer, running my own e...	within an hour	100%	63%	f	https://a0.muscache.com/im/pictures/user/c3e11...	https://a0.muscache.com/im/pictures/user/c3e11...	NaN	3.0	3.0	['email', 'phone']	t	t	NaN	Greater Dandenong	NaN	-37.951797	145.157050	Private room in home	Private room	2	NaN	0 shared baths	NaN	1.0	["Security cameras on property", "Smoke alarm"...	$80.00	1	365	1	1	365	365	1.0	365.0	NaN	t	4	34	64	339	2023-09-04	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	f	3	0	3	NaN

	id	scrape_id	host_id	host_listings_count	host_total_listings_count	neighbourhood_group_cleansed	latitude	longitude	accommodates	bathrooms	bedrooms	beds	minimum_nights	maximum_nights	minimum_minimum_nights	maximum_minimum_nights	minimum_maximum_nights	maximum_maximum_nights	minimum_nights_avg_ntm	maximum_nights_avg_ntm	calendar_updated	availability_30	availability_60	availability_90	availability_365	number_of_reviews	number_of_reviews_ltm	number_of_reviews_l30d	review_scores_rating	review_scores_accuracy	review_scores_cleanliness	review_scores_checkin	review_scores_communication	review_scores_location	review_scores_value	license	calculated_host_listings_count	calculated_host_listings_count_entire_homes	calculated_host_listings_count_private_rooms	calculated_host_listings_count_shared_rooms	reviews_per_month
count	2.318500e+04	2.318500e+04	2.318500e+04	23183.000000	23183.000000	0.0	23185.000000	23185.000000	23185.000000	0.0	16888.000000	23002.000000	23185.000000	23185.000000	23185.000000	23185.000000	2.318500e+04	2.318500e+04	23185.000000	2.318500e+04	0.0	23185.000000	23185.000000	23185.000000	23185.000000	23185.000000	23185.000000	23185.000000	18675.000000	18429.000000	18433.000000	18427.000000	18432.000000	18427.000000	18427.000000	0.0	23185.000000	23185.000000	23185.000000	23185.000000	18675.000000
mean	3.472407e+17	2.023090e+13	1.607668e+08	28.485140	49.072510	NaN	-37.828206	145.013870	3.659521	NaN	1.974420	2.127858	5.305499	560.395212	4.851369	6.089411	1.019553e+06	1.019580e+06	5.466875	1.019567e+06	NaN	11.431011	25.594609	40.532327	137.641320	30.803666	9.655769	0.716066	4.643992	4.748298	4.663471	4.779708	4.811204	4.824680	4.659304	NaN	17.263274	12.863403	4.246927	0.101100	1.370684
std	4.043073e+17	4.847761e+00	1.585790e+08	101.397683	199.137068	NaN	0.074543	0.150682	2.357757	NaN	1.076814	1.759124	29.116198	806.061758	25.774833	28.565941	4.676585e+07	4.676585e+07	26.640337	4.676585e+07	NaN	11.490477	23.304951	35.290262	131.909717	60.806538	17.740837	1.465085	0.696825	0.440869	0.493951	0.415675	0.410474	0.332126	0.470684	NaN	49.074356	33.107832	29.576002	0.967221	1.633770
min	1.080300e+04	2.023090e+13	7.834000e+03	1.000000	1.000000	NaN	-38.225690	144.518047	1.000000	NaN	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000e+00	1.000000e+00	1.000000	1.000000e+00	NaN	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	0.000000	NaN	1.000000	0.000000	0.000000	0.000000	0.010000
25%	2.359906e+07	2.023090e+13	3.039392e+07	1.000000	1.000000	NaN	-37.854080	144.956622	2.000000	NaN	1.000000	1.000000	1.000000	90.000000	1.000000	2.000000	1.800000e+02	3.650000e+02	1.000000	3.650000e+02	NaN	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	4.600000	4.700000	4.550000	4.740000	4.800000	4.790000	4.570000	NaN	1.000000	1.000000	0.000000	0.000000	0.240000
50%	4.792597e+07	2.023090e+13	9.879526e+07	2.000000	3.000000	NaN	-37.819440	144.977240	3.000000	NaN	2.000000	2.000000	2.000000	365.000000	2.000000	2.000000	1.125000e+03	1.125000e+03	2.000000	1.125000e+03	NaN	8.000000	25.000000	42.000000	91.000000	7.000000	2.000000	0.000000	4.830000	4.890000	4.820000	4.920000	4.950000	4.920000	4.780000	NaN	2.000000	1.000000	0.000000	0.000000	0.880000
75%	7.894182e+17	2.023090e+13	2.628817e+08	10.000000	15.000000	NaN	-37.802090	145.022480	4.000000	NaN	2.000000	3.000000	3.000000	1125.000000	3.000000	5.000000	1.125000e+03	1.125000e+03	3.700000	1.125000e+03	NaN	23.000000	49.000000	75.000000	267.000000	32.000000	13.000000	1.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	4.940000	NaN	8.000000	7.000000	1.000000	0.000000	1.990000
max	9.729284e+17	2.023090e+13	5.351276e+08	849.000000	1755.000000	NaN	-37.484800	145.844997	16.000000	NaN	14.000000	117.000000	1125.000000	99999.000000	1000.000000	1125.000000	2.147484e+09	2.147484e+09	1000.000000	2.147484e+09	NaN	30.000000	60.000000	90.000000	365.000000	987.000000	922.000000	40.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	NaN	327.000000	250.000000	251.000000	20.000000	44.060000

	listing_url	name	host_id	neighbourhood_cleansed	latitude	longitude	room_type	accommodates	bathrooms_text	bedrooms	beds	amenities	price	minimum_nights	number_of_reviews
120	https://www.airbnb.com/rooms/268620	Home in Caroline Springs · 1 bedroom	1407948	Melton	-37.73975	144.73742	Private room	1	NaN	NaN	NaN	[]	$72.00	1	0
211	https://www.airbnb.com/rooms/508590	Home in Balwyn North · ★4.63 · 1 bedroom	2255480	Boroondara	-37.79370	145.07935	Private room	1	NaN	NaN	NaN	[]	$50.00	1	16
6523	https://www.airbnb.com/rooms/26888053	Nature lodge in Mt Dandenong · ★4.78 · 1 bed	156749664	Yarra Ranges	-37.82976	145.35228	Hotel room	2	NaN	NaN	1.0	["Clothing storage: closet", "Microwave", "Hot...	$325.00	1	41
6537	https://www.airbnb.com/rooms/26904346	Nature lodge in Mt Dandenong · ★5.0 · 1 bedroo...	156749664	Yarra Ranges	-37.83003	145.35347	Hotel room	2	NaN	1.0	1.0	["Microwave", "Hot water kettle", "Carbon mono...	$364.00	1	3
7643	https://www.airbnb.com/rooms/31025816	Aparthotel in Docklands · ★4.21 · 2 bedrooms ·...	16970146	Melbourne	-37.81403	144.94222	Hotel room	4	NaN	2.0	2.0	["Microwave", "Coffee maker", "High chair", "H...	$282.00	1	19
8388	https://www.airbnb.com/rooms/34046314	Serviced apartment in Southbank · ★4.84 · 2 be...	255905173	Melbourne	-37.82815	144.96857	Hotel room	4	NaN	2.0	2.0	["Microwave", "Carbon monoxide alarm", "Hot wa...	$353.00	1	44
8641	https://www.airbnb.com/rooms/35914383	Serviced apartment in Williamstown · ★4.67 · 2...	270134235	Hobsons Bay	-37.85641	144.87878	Hotel room	4	NaN	2.0	3.0	["Smoke alarm", "Wifi", "Kitchen", "Dryer", "I...	$394.00	6	9
8696	https://www.airbnb.com/rooms/35717112	Home in Melton West · Studio · 1 bed	268666048	Melton	-37.66411	144.56059	Private room	2	NaN	NaN	1.0	["Smoke alarm", "Wifi", "Kitchen", "Private li...	$75.00	1	0
9051	https://www.airbnb.com/rooms/38076289	Aparthotel in Docklands · ★4.26 · 3 bedrooms ·...	16970146	Melbourne	-37.81403	144.94222	Hotel room	6	NaN	3.0	4.0	["Microwave", "Coffee maker", "High chair", "H...	$429.00	2	42
9238	https://www.airbnb.com/rooms/38883439	Serviced apartment in Point Cook · ★4.17 · 2 b...	296379242	Wyndham	-37.89366	144.76554	Hotel room	4	NaN	2.0	2.0	["Security cameras on property", "Smoke alarm"...	$269.00	1	6
10898	https://www.airbnb.com/rooms/43540050	Villa in Murrumbeena · ★4.91 · Studio · 1 bed	166664813	Glen Eira	-37.90158	145.06935	Private room	2	NaN	NaN	1.0	["Security cameras on property", "Wifi", "Kitc...	$40.00	7	23

	listing_url	name	host_id	neighbourhood_cleansed	latitude	longitude	room_type	accommodates	bathrooms_text	bedrooms	beds	amenities	price	minimum_nights	number_of_reviews
0	https://www.airbnb.com/rooms/10803	Rental unit in Brunswick East · ★4.49 · 1 bedr...	38901	Moreland	-37.766060	144.979510	Private room	2	1 shared bath	NaN	1.0	["Microwave", "Hot water kettle", "Drying rack...	$49.00	5	173
3	https://www.airbnb.com/rooms/41836	Home in Reservoir · ★4.71 · 1 bedroom · 1 bed ...	182833	Darebin	-37.697610	145.000660	Private room	2	1 shared bath	NaN	1.0	["Smoking allowed", "Smoke alarm", "Wifi", "Ki...	$40.00	7	159
7	https://www.airbnb.com/rooms/603007	Rental unit in South Yarra · ★4.76 · 1 bedroom...	2705870	Melbourne	-37.830220	144.986820	Private room	2	1 shared bath	NaN	1.0	["Private living room", "Carbon monoxide alarm...	$59.00	1	21
15	https://www.airbnb.com/rooms/628156	Condo in Port Melbourne · ★4.82 · 1 bedroom · ...	3119669	Port Phillip	-37.841580	144.937940	Private room	2	1 private bath	NaN	1.0	["Stainless steel gas stove", "Microwave", "AC...	$90.00	5	143
17	https://www.airbnb.com/rooms/44699	Condo in South Melbourne · ★4.75 · 1 bedroom ·...	189245	Port Phillip	-37.831557	144.970644	Private room	2	1 shared bath	NaN	1.0	["Stainless steel gas stove", "Microwave", "St...	$88.00	1	54
20	https://www.airbnb.com/rooms/1234561	Home in St Kilda · ★4.77 · 1 bedroom · 1 bed ·...	6731712	Port Phillip	-37.858440	144.988940	Private room	1	1 bath	NaN	1.0	["Smoking allowed", "Wifi", "Kitchen", "Dedica...	$39.00	2	23
27	https://www.airbnb.com/rooms/67211	Rental unit in Kew · ★4.82 · 1 bedroom · 1 bed...	326880	Boroondara	-37.803470	145.035830	Private room	1	1 shared bath	NaN	1.0	["Microwave", "Free street parking", "Lockbox"...	$50.00	2	225
30	https://www.airbnb.com/rooms/70328	Townhouse in Mordialloc · ★4.94 · 1 bedroom · ...	356006	Kingston	-38.007300	145.085440	Private room	2	1 private bath	NaN	1.0	["Clothing storage: closet", "Microwave", "Hot...	$65.00	1	147
32	https://www.airbnb.com/rooms/78143	Townhouse in Prahran · ★4.79 · 2 bedrooms · 2 ...	419767	Stonnington	-37.851620	144.989430	Private room	2	1 private bath	NaN	2.0	["Microwave", "Hot water kettle", "Shared outd...	$96.00	2	223
33	https://www.airbnb.com/rooms/671981	Home in Yarraville · 1 bedroom · 1 bed · 1 sha...	3395313	Maribyrnong	-37.815070	144.888950	Shared room	1	1 shared bath	NaN	1.0	["TV"]	$100.00	1	0

	listing_url	name	host_id	neighbourhood_cleansed	latitude	longitude	room_type	accommodates	bathrooms_text	bedrooms	beds	amenities	price	minimum_nights	number_of_reviews
1161	https://www.airbnb.com/rooms/6825713	Place to stay in Collingwood · ★4.82 · 2 beds ...	24685614	Yarra	-37.79515	144.985340	Entire home/apt	3	1 bath	NaN	2.0	["Microwave", "Drying rack for clothing", "Car...	$139.00	2	298
2327	https://www.airbnb.com/rooms/12293289	Castle in The Patch · ★4.97 · 1 bed · 1 bath	66261359	Yarra Ranges	-37.88414	145.391480	Entire home/apt	2	1 bath	NaN	1.0	["Microwave", "Hot water kettle", "BBQ grill",...	$489.00	1	149
2628	https://www.airbnb.com/rooms/13303352	Farm stay in Chum Creek/Healesville · ★4.95 · ...	21470508	Yarra Ranges	-37.61723	145.496610	Entire home/apt	2	1 bath	NaN	1.0	["Microwave", "Induction stove", "Hot water ke...	$141.00	1	393
4254	https://www.airbnb.com/rooms/19989578	Place to stay in Fitzroy · 2 beds · 1 shared bath	26178901	Yarra	-37.79769	144.980440	Private room	4	1 shared bath	NaN	2.0	["Wifi", "Kitchen", "Washer", "Hangers", "Esse...	$300.00	1	0
5314	https://www.airbnb.com/rooms/22488519	Place to stay in South Melbourne · 1 bed · 1 s...	526851	Port Phillip	-37.83458	144.968890	Private room	1	1 shared bath	NaN	1.0	["Smoke alarm", "Wifi", "Kitchen", "Dryer", "I...	$50.00	1	1
5503	https://www.airbnb.com/rooms/22814463	Place to stay in Badger Creek · ★4.85 · 1 bed ...	107919473	Yarra Ranges	-37.68255	145.557360	Entire home/apt	2	1 bath	NaN	1.0	["Kitchen", "Stove", "Patio or balcony", "Dish...	$199.00	2	185
5612	https://www.airbnb.com/rooms/23069274	Tiny home in Clematis · ★4.90 · 1 bed · 1 bath	62077033	Cardinia	-37.92948	145.410450	Entire home/apt	2	1 bath	NaN	1.0	["Microwave", "Hot water kettle", "Drying rack...	$130.00	1	263
6114	https://www.airbnb.com/rooms/24945953	Castle in The Patch · ★4.92 · 1 bed · 1 bath	66261359	Yarra Ranges	-37.88362	145.390070	Entire home/apt	2	1 bath	NaN	1.0	["Microwave", "Induction stove", "Hot water ke...	$494.00	1	38
6523	https://www.airbnb.com/rooms/26888053	Nature lodge in Mt Dandenong · ★4.78 · 1 bed	156749664	Yarra Ranges	-37.82976	145.352280	Hotel room	2	0 baths	NaN	1.0	["Clothing storage: closet", "Microwave", "Hot...	$325.00	1	41
8331	https://www.airbnb.com/rooms/34515725	Tiny home in Beaumaris · 1 bed · 1 shared bath	29665549	Bayside	-37.99194	145.041330	Private room	2	1 shared bath	NaN	1.0	["Smoke alarm", "Kitchen", "Hot tub", "Carbon ...	$150.00	1	0
8534	https://www.airbnb.com/rooms/34977090	Farm stay in Yarra Junction · ★4.83 · 1 bed · ...	25918877	Yarra Ranges	-37.78794	145.639140	Entire home/apt	2	1 bath	NaN	1.0	["Microwave", "Hot water kettle", "BBQ grill",...	$206.00	1	166
8712	https://www.airbnb.com/rooms/36046114	Tiny home in Sandringham · ★4.93 · 3 beds · 1 ...	229196502	Bayside	-37.95524	145.021260	Entire home/apt	6	1 bath	NaN	3.0	["Integrated Fisher and Paykel refrigerator", ...	$220.00	3	111
8980	https://www.airbnb.com/rooms/37500292	Tiny home in East Warburton · ★4.81 · 3 beds ·...	262886891	Yarra Ranges	-37.73093	145.752710	Entire home/apt	3	1 bath	NaN	3.0	["BBQ grill", "Breakfast", "Barbecue utensils"...	$165.00	2	34
9104	https://www.airbnb.com/rooms/38117602	Farm stay in Healesville · ★4.40 · 1 bed · 1.5...	100564438	Yarra Ranges	-37.67870	145.515640	Private room	1	1.5 baths	NaN	1.0	["Smoking allowed", "Smoke alarm", "Wifi", "Pr...	$110.00	300	6
9345	https://www.airbnb.com/rooms/38979142	Camper/RV in St Kilda · 1 bed · 0 baths	56324731	Port Phillip	-37.86176	144.972330	Entire home/apt	3	0 baths	NaN	1.0	["Smoking allowed", "Kitchen", "First aid kit"...	$35.00	120	0
9806	https://www.airbnb.com/rooms/40377799	Tent in Healesville · ★4.81 · 1 bed · 1 bath	18768614	Yarra Ranges	-37.64708	145.534910	Entire home/apt	2	1 bath	NaN	1.0	["Smoke alarm", "Patio or balcony", "Dishes an...	$200.00	1	194
9960	https://www.airbnb.com/rooms/40510306	Barn in Dixons Creek · ★4.95 · 1 bed · 1 bath	272422171	Yarra Ranges	-37.61312	145.403040	Entire home/apt	2	1 bath	NaN	1.0	["Microwave", "Breakfast", "Coffee maker", "Fr...	$150.00	2	65
10179	https://www.airbnb.com/rooms/41382311	Hut in Millgrove · 3 beds · 1 bath	15783446	Yarra Ranges	-37.75091	145.648950	Entire home/apt	5	1 bath	NaN	3.0	["Kitchen", "Essentials", "Free parking on pre...	$80.00	50	0
11188	https://www.airbnb.com/rooms/46096289	Tent in The Patch · ★4.82 · 1 bed · 1 bath	31068242	Yarra Ranges	-37.88634	145.399630	Entire home/apt	2	1 bath	NaN	1.0	["Bluetooth sound system", "Outdoor shower", "...	$185.00	1	130
11768	https://www.airbnb.com/rooms/48741282	Camper/RV in Lilydale · 1 bed · 1 bath	7104573	Yarra Ranges	-37.75890	145.391040	Entire home/apt	2	1 bath	NaN	1.0	["Security cameras on property", "Smoke alarm"...	$170.00	2	0
13040	https://www.airbnb.com/rooms/53895756	Place to stay in Alphington · ★4.92 · 2 beds ·...	238794429	Darebin	-37.77995	145.031630	Entire home/apt	3	1 bath	NaN	2.0	["AC - split type ductless system", "Lockbox",...	$137.00	2	87
16833	https://www.airbnb.com/rooms/767566122456779194	Tiny home in Healesville · ★4.97 · 1 bed · 1 bath	50611336	Yarra Ranges	-37.65515	145.513968	Entire home/apt	2	1 bath	NaN	1.0	["Wifi", "Kitchen", "BBQ grill", "Refrigerator...	$179.00	1	30

Data Analysis - Airbnb Melbourne Australia¶

1. Setting up the environment¶

2. Data Collection¶

3. Data Exploration¶

Understanding the DataFrame¶

Descriptive Statistics¶

Data Quality Assessment¶

4. Data Cleaning¶

Remove Unnecessary Columns¶

Handling Missing Values¶

Data Type Conversion¶

Handling Inconsistent Data¶

Duplicate data¶

Outliers¶

Checking 'beds' column¶

Checking 'price' column¶

Checking 'minimum_nights' column¶

5. Exploratory Data Analysis¶

Univariate Analysis¶

1. Who are the top 10 hosts with the most listings?¶

2. Where are the top 10 Melbourne Airbnbs listings located?¶

3. How are the room type distributed in the listings?¶

4. What are the 5 most common numbers of bedrooms in the listings?¶

5. What are the 5 most frequent minimum_nights?¶

6. What are the four amenities most commonly provided?¶

Bivariate Analysis¶

7. Which room type in Melbourne's Airbnb listings has the highest number of reviews?¶

8. The average number of amenities varies significantly across different room types?¶

9. Is the average price per night higher for private room than for shared rooms?¶

Correlation Analysis¶

10. Properties with more reviews might be priced higher due to their popularity?¶

11. Listings with more amenities might be priced higher?¶

Dashboard¶

Conclusion¶

Thanks for your time!¶

	id	listing_url	scrape_id	last_scraped	source	name	description	neighborhood_overview	picture_url	host_id	host_url	host_name	host_since	host_location	host_about	host_response_time	host_response_rate	host_acceptance_rate	host_is_superhost	host_thumbnail_url	host_picture_url	host_neighbourhood	host_listings_count	host_total_listings_count	host_verifications	host_has_profile_pic	host_identity_verified	neighbourhood	neighbourhood_cleansed	neighbourhood_group_cleansed	latitude	longitude	property_type	room_type	accommodates	bathrooms	bathrooms_text	bedrooms	beds	amenities	price	minimum_nights	maximum_nights	minimum_minimum_nights	maximum_minimum_nights	minimum_maximum_nights	maximum_maximum_nights	minimum_nights_avg_ntm	maximum_nights_avg_ntm	calendar_updated	has_availability	availability_30	availability_60	availability_90	availability_365	calendar_last_scraped	number_of_reviews	number_of_reviews_ltm	number_of_reviews_l30d	first_review	last_review	review_scores_rating	review_scores_accuracy	review_scores_cleanliness	review_scores_checkin	review_scores_communication	review_scores_location	review_scores_value	license	instant_bookable	calculated_host_listings_count	calculated_host_listings_count_entire_homes	calculated_host_listings_count_private_rooms	reviews_per_month
0	10803	https://www.airbnb.com/rooms/10803	20230904181306	2023-09-05	city scrape	Rental unit in Brunswick East · ★4.49 · 1 bedr...	A large air conditioned room with firm queen s...	This hip area is a crossroads between two grea...	https://a0.muscache.com/pictures/e5f30dd1-ac57...	38901	https://www.airbnb.com/users/show/38901	Lindsay	2009-09-16	Melbourne, Australia	As an artist working in animation and video I ...	within a few hours	100%	95%	f	https://a0.muscache.com/im/pictures/user/6860c...	https://a0.muscache.com/im/pictures/user/6860c...	Brunswick	1.0	1.0	['email', 'phone']	t	t	Brunswick East, Victoria, Australia	Moreland	NaN	-37.76606	144.97951	Private room in rental unit	Private room	2	NaN	1 shared bath	NaN	1.0	["Microwave", "Hot water kettle", "Drying rack...	$49.00	5	14	1	5	14	14	5.0	14.0	NaN	t	4	13	35	201	2023-09-05	173	21	0	2013-01-12	2023-07-19	4.49	4.65	3.98	4.72	4.69	4.66	4.61	NaN	f	1	0	1	1.33
1	12936	https://www.airbnb.com/rooms/12936	20230904181306	2023-09-05	previous scrape	Rental unit in St Kilda · ★4.68 · 1 bedroom · ...	RIGHT IN THE HEART OF ST KILDA! It doesn't get...	A stay at our apartment means you can enjoy so...	https://a0.muscache.com/pictures/59701/2e8cdaf...	50121	https://www.airbnb.com/users/show/50121	The A2C Team	2009-10-31	Melbourne, Australia	NaN	NaN	NaN	NaN	f	https://a0.muscache.com/im/pictures/user/3894a...	https://a0.muscache.com/im/pictures/user/3894a...	St Kilda	10.0	20.0	['email', 'phone', 'work_email']	t	t	St Kilda, Victoria, Australia	Port Phillip	NaN	-37.85999	144.97662	Entire rental unit	Entire home/apt	2	NaN	1 bath	1.0	1.0	["Microwave", "Lockbox", "Hot water", "Smoke a...	$95.00	3	14	3	3	14	14	3.0	14.0	NaN	t	0	0	0	0	2023-09-05	42	0	0	2010-08-04	2020-03-15	4.68	4.78	4.71	4.83	4.83	4.78	4.66	NaN	f	10	10	0	0.26
2	38271	https://www.airbnb.com/rooms/38271	20230904181306	2023-09-04	city scrape	Rental unit in Berwick · ★4.86 · 3 bedrooms · ...	No children under 13 will be accepted in your ...	Our street is quiet & secluded but within walk...	https://a0.muscache.com/pictures/1182791/3bf4b...	164193	https://www.airbnb.com/users/show/164193	Daryl & Dee	2010-07-12	Berwick, Australia	We are an active couple who work from home and...	within an hour	100%	91%	t	https://a0.muscache.com/im/pictures/user/29c38...	https://a0.muscache.com/im/pictures/user/29c38...	NaN	1.0	1.0	['email', 'phone', 'work_email']	t	t	Berwick, Victoria, Australia	Casey	NaN	-38.05723	145.33982	Entire rental unit	Entire home/apt	5	NaN	1 bath	3.0	3.0	["Microwave", "Hot water kettle", "Drying rack...	$116.00	1	14	1	1	14	14	1.0	14.0	NaN	t	18	40	64	325	2023-09-04	228	36	3	2010-11-24	2023-08-26	4.86	4.92	4.98	4.91	4.94	4.90	4.88	NaN	f	1	1	0	1.47

	0	1	2	3	4
0	Rental unit in Brunswick East	★4.49	1 bedroom	1 bed	1 shared bath
3	Home in Reservoir	★4.71	1 bedroom	1 bed	1 shared bath
7	Rental unit in South Yarra	★4.76	1 bedroom	1 bed	1 shared bath
15	Condo in Port Melbourne	★4.82	1 bedroom	1 bed	1 private bath
17	Condo in South Melbourne	★4.75	1 bedroom	1 bed	1 shared bath
20	Home in St Kilda	★4.77	1 bedroom	1 bed	1 bath
27	Rental unit in Kew	★4.82	1 bedroom	1 bed	1 shared bath
30	Townhouse in Mordialloc	★4.94	1 bedroom	1 bed	1 private bath
32	Townhouse in Prahran	★4.79	2 bedrooms	2 beds	1 private bath
33	Home in Yarraville	1 bedroom	1 bed	1 shared bath	None

	listing_url	name	host_id	neighbourhood_cleansed	latitude	longitude	room_type	accommodates	bathrooms_text	bedrooms	beds	amenities	price	minimum_nights	number_of_reviews
783	https://www.airbnb.com/rooms/5573248	Home in Richmond · ★4.89 · 2 bedrooms · 1.5 baths	6518033	Yarra	-37.81748	144.99520	Entire home/apt	4	1.5 baths	2.0	NaN	["Stainless steel gas stove", "Microwave", "St...	$206.00	1	264
845	https://www.airbnb.com/rooms/5096866	Home in Coburg North · ★4.71 · 1 bedroom · 1 s...	26336840	Moreland	-37.72044	144.96969	Private room	3	1 shared bath	1.0	NaN	["Smoking allowed", "Smoke alarm", "Wifi", "Ki...	$42.00	5	43
2430	https://www.airbnb.com/rooms/12633686	Townhouse in Mulgrave · 4 bedrooms · 2.5 baths	40094786	Monash	-37.92501	145.18905	Entire home/apt	6	2.5 baths	4.0	NaN	["Microwave", "Hot water kettle", "BBQ grill",...	$380.00	15	0
2780	https://www.airbnb.com/rooms/13592574	Rental unit in Prahran · ★4.94 · 1 bedroom · 1...	78547489	Stonnington	-37.85131	144.98881	Entire home/apt	4	1 bath	1.0	NaN	["Stainless steel gas stove", "Microwave", "Ho...	$160.00	4	35
2895	https://www.airbnb.com/rooms/14094124	Townhouse in Brighton · ★5.0 · 2 bedrooms · 2 ...	84806429	Bayside	-37.88982	145.00013	Entire home/apt	4	2 baths	2.0	NaN	["Wifi", "Kitchen", "Dryer", "Iron", "First ai...	$175.00	3	3
3419	https://www.airbnb.com/rooms/16440438	Cottage in Hoddles Creek · ★5.0 · 2 bedrooms ·...	88051903	Yarra Ranges	-37.81570	145.56603	Entire home/apt	4	1 bath	2.0	NaN	["Microwave", "Hot water kettle", "AC - split ...	$330.00	2	53
3478	https://www.airbnb.com/rooms/16233947	Home in Clifton Hill · ★5.0 · 3 bedrooms · 2 b...	33071051	Yarra	-37.78820	145.00014	Entire home/apt	4	2 baths	3.0	NaN	["Pool", "Smoke alarm", "Wifi", "Kitchen", "Dr...	$350.00	7	12
5786	https://www.airbnb.com/rooms/23931198	Villa in Carrum · ★4.83 · 3 bedrooms · 1 share...	131401047	Kingston	-38.08143	145.14034	Private room	1	1 shared bath	3.0	NaN	["Smoke alarm", "Wifi", "Kitchen", "Bed linens...	$38.00	3	6
8229	https://www.airbnb.com/rooms/33601421	Home in Kew · ★5.0 · 2 bedrooms · 1.5 shared b...	50264189	Boroondara	-37.79356	145.03792	Private room	2	1.5 shared baths	2.0	NaN	["Microwave", "BBQ grill", "Private living roo...	$69.00	1	3
9302	https://www.airbnb.com/rooms/38472816	Condo in Southbank · ★4.53 · 1 bedroom · 1 bath	155404054	Melbourne	-37.82225	144.96119	Entire home/apt	4	1 bath	1.0	NaN	["Microwave", "Bluetooth sound system", "Harbo...	$122.00	1	74

	host_id	latitude	longitude	accommodates	bedrooms	beds	price	minimum_nights	number_of_reviews
count	2.318000e+04	23180.000000	23180.000000	23180.000000	23180.000000	23180.000000	23180.000000	23180.000000	23180.000000
mean	1.607798e+08	-37.828200	145.013867	3.659232	1.717990	2.120449	230.782442	5.305824	30.810224
std	1.585869e+08	0.074548	0.150693	2.357892	1.073017	1.755538	1333.728563	29.119214	60.811455
min	7.834000e+03	-38.225690	144.518047	1.000000	0.000000	1.000000	0.000000	1.000000	0.000000
25%	3.038696e+07	-37.854085	144.956621	2.000000	1.000000	1.000000	95.000000	1.000000	1.000000
50%	9.884258e+07	-37.819435	144.977230	3.000000	1.000000	2.000000	149.000000	2.000000	7.000000
75%	2.628817e+08	-37.802090	145.022482	4.000000	2.000000	3.000000	226.000000	3.000000	32.000000
max	5.351276e+08	-37.484800	145.844997	16.000000	14.000000	117.000000	104983.000000	1125.000000	987.000000

	listing_url	name	host_id	neighbourhood_cleansed	latitude	longitude	room_type	accommodates	bathrooms_text	bedrooms	beds	amenities	price	minimum_nights	number_of_reviews
11279	https://www.airbnb.com/rooms/46266224	Vacation home in Yarra Glen · 10 bedrooms · 22...	107232439	Yarra Ranges	-37.622600	145.372260	Entire home/apt	16	9 baths	10	22	["Microwave", "BBQ grill", "Barbecue utensils"...	2400.0	2	2
15169	https://www.airbnb.com/rooms/690072715126723461	casa particular in Melbourne · 3 bedrooms · 11...	457710697	Melbourne	-37.815070	144.962610	Entire home/apt	2	2.5 baths	3	117	["Smoking allowed", "Window AC unit", "Pets al...	19045.0	1	0
19381	https://www.airbnb.com/rooms/857121112148324865	Home in Deer Park · 3 bedrooms · 31 beds · 2 b...	485621814	Brimbank	-37.761819	144.772296	Entire home/apt	16	2 baths	3	31	["Microwave", "Hot water kettle", "BBQ grill",...	169.0	1	1

	host_id	latitude	longitude	accommodates	bedrooms	beds	price	minimum_nights	number_of_reviews
count	2.317900e+04	23179.000000	23179.000000	23179.000000	23179.000000	23179.000000	23179.000000	23179.000000	23179.000000
mean	1.607670e+08	-37.828201	145.013869	3.659304	1.717934	2.115492	229.970749	5.306010	30.811554
std	1.585783e+08	0.074549	0.150696	2.357918	1.073007	1.585129	1328.019560	29.119828	60.812430
min	7.834000e+03	-38.225690	144.518047	1.000000	0.000000	1.000000	0.000000	1.000000	0.000000
25%	3.038696e+07	-37.854090	144.956621	2.000000	1.000000	1.000000	95.000000	1.000000	1.000000
50%	9.883592e+07	-37.819440	144.977230	3.000000	1.000000	2.000000	149.000000	2.000000	7.000000
75%	2.628817e+08	-37.802090	145.022485	4.000000	2.000000	3.000000	226.000000	3.000000	32.000000
max	5.351276e+08	-37.484800	145.844997	16.000000	14.000000	31.000000	104983.000000	1125.000000	987.000000

	listing_url	name	host_id	neighbourhood_cleansed	latitude	longitude	room_type	accommodates	bathrooms_text	bedrooms	beds	amenities	price	minimum_nights	number_of_reviews
367	https://www.airbnb.com/rooms/3094430	Home in Ormond · 1 bedroom · 1 bed · 1 bath	15736128	Glen Eira	-37.90021	145.03742	Private room	1	1 bath	1	1	["Wifi", "Kitchen", "Washer", "First aid kit",...	26.0	1	0
413	https://www.airbnb.com/rooms/3194833	Rental unit in Southbank · 1 bedroom · 2 beds ...	1424387	Port Phillip	-37.82871	144.95737	Shared room	2	1 shared bath	1	2	["Pool", "Smoke alarm", "Wifi", "Kitchen", "Ho...	24.0	1	1
727	https://www.airbnb.com/rooms/4786567	Rental unit in South Yarra · ★4.67 · 1 bedroom...	24674881	Melbourne	-37.83538	144.98543	Private room	1	1 bath	1	1	["Smoke alarm", "Wifi", "Kitchen", "Dryer", "E...	25.0	7	3
761	https://www.airbnb.com/rooms/5196283	Rental unit in Flemington · ★4.60 · 1 bedroom ...	19752824	Melbourne	-37.78868	144.92314	Private room	3	1 bath	1	2	["Smoke alarm", "Wifi", "Dryer", "Pets allowed...	20.0	3	5
1091	https://www.airbnb.com/rooms/7207612	Home in Northcote · 1 bedroom · 1 bed · 1 shar...	37741609	Darebin	-37.76533	145.00940	Private room	2	1 shared bath	1	1	["Wifi", "Smoke alarm", "Kitchen", "Essentials...	25.0	12	0
1118	https://www.airbnb.com/rooms/7253120	Home in Richmond · 1 bedroom · 1 bed · 1 bath	27460296	Yarra	-37.81993	144.99620	Private room	2	1 bath	1	1	["Smoke alarm", "Wifi", "Kitchen", "Washer", "...	28.0	3	2
1182	https://www.airbnb.com/rooms/6855196	Home in Camberwell · 1 bedroom · 1 bed · 1 bath	35925193	Boroondara	-37.84727	145.07037	Private room	2	1 bath	1	1	["Smoking allowed", "Wifi", "Kitchen", "Washer...	13.0	1	0
1228	https://www.airbnb.com/rooms/7440879	Home in Coburg · ★4.88 · 1 bedroom · 1 bed · 1...	33559438	Merri-bek	-37.73810	144.97693	Shared room	1	1.5 shared baths	1	1	["Pool", "Smoke alarm", "Wifi", "Kitchen", "Wa...	25.0	1	8
1300	https://www.airbnb.com/rooms/7060419	Home in Camberwell · 1 bedroom · 1 bed · 1 bath	37024203	Boroondara	-37.84818	145.09497	Private room	2	1 bath	1	1	["Wifi", "Smoke alarm", "Kitchen", "Essentials...	20.0	1	0
1434	https://www.airbnb.com/rooms/8905856	Home in Clifton Hill · 1 bedroom · 1 bed · 1 bath	46582678	Yarra	-37.78931	144.99854	Private room	2	1 bath	1	1	["Wifi", "Kitchen", "Smoke alarm", "TV", "Free...	26.0	1	0
1468	https://www.airbnb.com/rooms/8375432	Home in Carlton North · 1 bedroom · 1 bed · 1 ...	44142350	Yarra	-37.78067	144.97007	Private room	1	1 bath	1	1	["Wifi", "Smoke alarm", "Kitchen", "Breakfast"...	22.0	12	0
1476	https://www.airbnb.com/rooms/8394057	Home in Highett · 1 bedroom · 1 bed · 1.5 baths	44229140	Bayside	-37.94779	145.03006	Private room	1	1.5 baths	1	1	["Wifi", "Smoke alarm", "Kitchen", "Washer", "...	25.0	1	1
1539	https://www.airbnb.com/rooms/8656155	Rental unit in Thornbury · 1 bedroom · 1 bed ·...	45489433	Darebin	-37.75685	145.01064	Private room	1	1 bath	1	1	["Wifi", "Kitchen", "Washer", "Essentials", "F...	27.0	7	0
1822	https://www.airbnb.com/rooms/10000335	Place to stay in Clayton · 2 bedrooms · 1 bed ...	51373610	Monash	-37.91737	145.13953	Private room	1	3 shared baths	2	1	["Smoke alarm", "Wifi", "Kitchen", "Gym", "Iro...	26.0	3	0
1952	https://www.airbnb.com/rooms/10572460	Home in Altona Meadows · 1 bedroom · 1 bed · 1...	51890204	Hobsons Bay	-37.87756	144.76848	Private room	2	1 bath	1	1	["Smoke alarm", "Wifi", "Kitchen", "Dryer", "I...	26.0	3	2

	listing_url	name	host_id	neighbourhood_cleansed	latitude	longitude	room_type	accommodates	bathrooms_text	bedrooms	beds	amenities	price	minimum_nights	number_of_reviews
78	https://www.airbnb.com/rooms/1353588	Townhouse in Richmond · ★4.79 · Studio · 2 bed...	6362922	Yarra	-37.82157	144.99694	Private room	1	0 shared baths	0	2	["Wifi", "Kitchen", "Iron", "Hangers", "Breakf...	20000.0	2	95
705	https://www.airbnb.com/rooms/4767182	Home in Malvern · 5 bedrooms · 5 beds · 4 baths	11914644	Stonnington	-37.84542	145.03280	Entire home/apt	10	4 baths	5	5	["Microwave", "Coffee maker", "Host greets you...	3304.0	1	0
1246	https://www.airbnb.com/rooms/7490588	Rental unit in Melbourne · ★4.49 · 2 bedrooms ...	27519695	Melbourne	-37.81474	144.95128	Entire home/apt	8	1 bath	2	2	["Microwave", "Carbon monoxide alarm", "Lockbo...	6825.0	400	400
1885	https://www.airbnb.com/rooms/10476673	Home in Heatherton · 3 bedrooms · 3 beds · 2.5...	31713704	Kingston	-37.95436	145.08372	Entire home/apt	6	2.5 baths	3	3	["Pool", "Smoke alarm", "Wifi", "Kitchen", "Dr...	2243.0	7	0
2347	https://www.airbnb.com/rooms/12100235	Home in Healesville · ★4.90 · 10 bedrooms · 13...	64874108	Yarra Ranges	-37.65557	145.51727	Entire home/apt	16	9.5 baths	10	13	["Microwave", "Hot water kettle", "BBQ grill",...	2127.0	2	49
2461	https://www.airbnb.com/rooms/12719024	Home in Reservoir · 2 bedrooms · 2 beds · 1 bath	69151339	Darebin	-37.72707	144.99738	Entire home/apt	4	1 bath	2	2	["Smoking allowed", "Smoke alarm", "Wifi", "Ki...	12624.0	10	1
2465	https://www.airbnb.com/rooms/12736588	Home in Thornbury · 1 bedroom · 1 bed · 1.5 baths	47493458	Darebin	-37.75935	145.00777	Private room	2	1.5 baths	1	1	["Smoking allowed", "Smoke alarm", "Wifi", "Ki...	2500.0	3	0
2999	https://www.airbnb.com/rooms/14919472	Rental unit in Southbank · ★5.0 · 4 bedrooms ·...	29183827	Melbourne	-37.82225	144.96365	Entire home/apt	6	3.5 baths	4	5	["Smoke alarm", "Wifi", "Kitchen", "Iron", "Ca...	2057.0	7	4
3491	https://www.airbnb.com/rooms/16260007	Home in Reservoir · ★4.0 · 1 bedroom · 1 bed ·...	96127123	Darebin	-37.69922	145.01132	Private room	2	1 bath	1	1	["Smoking allowed", "Smoke alarm", "Wifi", "Ki...	13379.0	1	3
3777	https://www.airbnb.com/rooms/18043096	Rental unit in Saint Kilda · ★4.58 · 2 bedroom...	105279173	Port Phillip	-37.86593	144.98351	Private room	2	1 shared bath	2	1	["Smoke alarm", "Wifi", "Kitchen", "Bed linens...	5500.0	14	12

	host_id	latitude	longitude	accommodates	bedrooms	beds	price	minimum_nights	number_of_reviews
count	2.309400e+04	23094.000000	23094.000000	23094.000000	23094.000000	23094.000000	23094.000000	23094.000000	23094.000000
mean	1.608007e+08	-37.828205	145.013783	3.649952	1.712999	2.109725	197.130423	5.235472	30.866372
std	1.586302e+08	0.074545	0.150722	2.346839	1.064941	1.572811	187.706796	28.747963	60.825704
min	7.834000e+03	-38.225690	144.518047	1.000000	0.000000	1.000000	30.000000	1.000000	0.000000
25%	3.039534e+07	-37.854127	144.956592	2.000000	1.000000	1.000000	99.000000	1.000000	1.000000
50%	9.881559e+07	-37.819410	144.977188	3.000000	1.000000	2.000000	149.000000	2.000000	7.000000
75%	2.628817e+08	-37.802066	145.022396	4.000000	2.000000	3.000000	225.000000	3.000000	32.000000
max	5.351276e+08	-37.484800	145.844997	16.000000	14.000000	31.000000	2000.000000	1125.000000	987.000000

	listing_url	name	host_id	neighbourhood_cleansed	latitude	longitude	room_type	accommodates	bathrooms_text	bedrooms	beds	amenities	price	minimum_nights	number_of_reviews
110	https://www.airbnb.com/rooms/900838	Loft in Richmond · ★4.88 · 1 bedroom · 1 bed ·...	4812182	Yarra	-37.82286	144.99065	Entire home/apt	2	1 bath	1	1	["Smoke alarm", "Wifi", "Kitchen", "Dryer", "I...	232.0	803	141
590	https://www.airbnb.com/rooms/4632666	Rental unit in Melbourne · ★4.62 · 2 bedrooms ...	4924814	Melbourne	-37.81572	144.95447	Entire home/apt	5	1 bath	2	3	["Microwave", "BBQ grill", "Free street parkin...	149.0	700	165
650	https://www.airbnb.com/rooms/4844270	Rental unit in South Yarra · 1 bedroom · 1 bed...	21300152	Melbourne	-37.82967	144.98577	Entire home/apt	2	1 bath	1	1	["Wifi", "Kitchen", "Iron", "Hangers", "Essent...	125.0	500	1
1974	https://www.airbnb.com/rooms/10770086	Rental unit in Travancore · 1 bedroom · 1 bed ...	54793045	Melbourne	-37.78452	144.93970	Private room	2	1 bath	1	1	["Smoke alarm", "Wifi", "Kitchen", "Gym", "Iro...	34.0	900	1
2922	https://www.airbnb.com/rooms/14833595	Rental unit in Melbourne · ★4.69 · 3 bedrooms ...	4924814	Melbourne	-37.81624	144.95495	Entire home/apt	9	2 baths	3	5	["Microwave", "Free street parking", "High cha...	269.0	600	125
2950	https://www.airbnb.com/rooms/14581093	Rental unit in Southbank · ★4.78 · 2 bedrooms ...	4924814	Melbourne	-37.82821	144.95896	Entire home/apt	6	2 baths	2	3	["Free street parking", "High chair", "Hot wat...	269.0	1124	75
2971	https://www.airbnb.com/rooms/14893028	Rental unit in Melbourne · ★4.76 · 3 bedrooms ...	4924814	Melbourne	-37.81532	144.95331	Entire home/apt	8	2 baths	3	5	["Microwave", "Free street parking", "Lockbox"...	299.0	600	137
4570	https://www.airbnb.com/rooms/21035543	Home in Wantirna · 1 bedroom · 1 bed · 1 priva...	42784943	Knox	-37.84083	145.22844	Private room	2	1 private bath	1	1	["Smoking allowed", "Smoke alarm", "Wifi", "Ki...	40.0	1000	1
4669	https://www.airbnb.com/rooms/21194478	Rental unit in Southbank · ★4.70 · 3 bedrooms ...	4924814	Melbourne	-37.82727	144.96079	Entire home/apt	9	2 baths	3	5	["Microwave", "Free street parking", "High cha...	209.0	700	55
4968	https://www.airbnb.com/rooms/21792086	Rental unit in Melbourne · ★4.30 · 1 bedroom ·...	156159929	Melbourne	-37.80936	144.96001	Private room	2	1 shared bath	1	1	["Pool", "Wifi", "Kitchen", "Hangers", "Essent...	55.0	1000	52

	Host ID	Number of Listings	Percentage of Listings
1	90729398	327	1.42
2	279001183	250	1.08
3	343442154	154	0.67
4	1739996	98	0.42
5	412803321	80	0.35
6	22860147	80	0.35
7	446080599	77	0.33
8	760849	73	0.32
9	392306676	69	0.30
10	10190138	66	0.29

	Neighbourhood	Number of Listings	Percentage of Listings
1	Melbourne	7246	31.40
2	Port Phillip	2723	11.80
3	Yarra	1771	7.67
4	Stonnington	1501	6.50
5	Yarra Ranges	1106	4.79
6	Merri-bek	940	4.07
7	Monash	590	2.56
8	Boroondara	586	2.54
9	Whitehorse	582	2.52
10	Wyndham	575	2.49

	Number of Bedroom	Number of Listings	Percentage of Listings
1	1	11386	49.33
2	2	6926	30.01
3	3	2527	10.95
4	4	1051	4.55
5	0	748	3.24

	Minimum Nights	Number of Listings	Percentage of Listings
1	1	7895	34.21
2	2	7325	31.74
3	3	3008	13.03
4	7	1257	5.45
5	5	1021	4.42

	Room Type	Number of Listings	Percentage of Listings
1	Entire home/apt	16803	72.80
2	Private room	5911	25.61
3	Shared room	277	1.20
4	Hotel room	89	0.39

	Amenity	Number of Amenities	Percentage of Amenities
1	Kitchen	21865	94.74
2	Smoke alarm	21605	93.61
3	Wifi	20307	87.99
4	Essentials	20017	86.73

	Room Type	Number of Reviews	Percentage of Reviews
0	Entire home/apt	613773	86.20
1	Shared room	94852	13.32
2	Private room	1765	0.25
3	Hotel room	1674	0.24

	Room Type	Average Amenities
1	Entire home/apt	35.14
2	Private room	24.00
3	Hotel room	23.89
4	Shared room	18.20

	Room Type	Average Price
1	Private room	100.22
2	Shared room	117.17
3	Entire home/apt	232.23
4	Hotel room	243.19

	accommodates	bedrooms	beds	price	minimum_nights	number_of_reviews
accommodates	1.000000	0.811764	0.861758	0.482950	-0.018598	0.022064
bedrooms	0.811764	1.000000	0.785499	0.470027	-0.003285	-0.047187
beds	0.861758	0.785499	1.000000	0.438041	-0.005333	-0.000953
price	0.482950	0.470027	0.438041	1.000000	0.022074	-0.047232
minimum_nights	-0.018598	-0.003285	-0.005333	0.022074	1.000000	-0.027315
number_of_reviews	0.022064	-0.047187	-0.000953	-0.047232	-0.027315	1.000000